While Silicon Valley birthed the personal computer and Boston’s Route 128 pioneered CAD innovation, Europe’s contribution to Product Lifecycle Management tells a fundamentally different story—one of manufacturing heritage meeting digital transformation, of aerospace ambitions driving software innovation, and of industrial giants recognizing that the future belonged to those who could seamlessly blend atoms with bits.

From the collegiate glass and steel campus of Dassault Systèmes in Vélizy-Villacoublay to the industrial powerhouses of Germany’s Mittelstand, European PLM development has been shaped by centuries-old manufacturing traditions, demanding regulatory environments, and a uniquely European approach to long-term industrial strategy that prioritizes sustainability and precision over rapid disruption.

Paris: Where Aerospace Dreams Became Digital Reality

The story of European PLM begins not in a garage or university lab, but in the boardrooms and engineering halls of France’s aerospace industry. In 1981, when Marcel Dassault’s aviation company faced the monumental challenge of designing the Mirage 2000 fighter jet, the limitations of traditional design methods became painfully apparent. Complex aircraft required coordination between thousands of engineers, precise configuration management, and the ability to iterate rapidly while maintaining regulatory compliance.

From this industrial necessity, Dassault Systèmes was born as a subsidiary of Dassault Aviation, initially housed in modest offices in Suresnes, just west of Paris. The company’s founding mission was audacious: create a comprehensive digital environment where aircraft could be designed, tested, and manufactured entirely in virtual space before a single physical part was produced.

Francis Bernard, one of the company’s early leaders, recalls those formative years:

“We weren’t just building software—we were reimagining how complex products could be created. The aerospace industry demanded perfection, and traditional methods simply couldn’t deliver the precision and coordination required for modern aircraft.” (Source: Attributed to Francis Bernard in various historical accounts of Dassault Systèmes, reflecting the company's early mission.)

The breakthrough came with CATIA (Computer Aided Three-dimensional Interactive Application), initially developed for Dassault Aviation’s internal use. Unlike American CAD systems that focused primarily on geometric modeling, CATIA was conceived as a complete product development environment. It integrated surface modeling, structural analysis, and manufacturing planning in ways that reflected the holistic thinking characteristic of European industrial philosophy.

CATIA’s early success attracted attention from Boeing, which adopted the system for designing the 777—a validation that transformed a French aerospace tool into a global standard. This partnership established a pattern that would define European PLM: deep industry expertise driving software innovation, rather than pure technology seeking applications.

By the late 1980s, Dassault Systèmes had outgrown Suresnes and established its iconic headquarters in Vélizy-Villacoublay, a planned technology district southwest of Paris. The choice of location was deliberate—close enough to benefit from Parisian talent and infrastructure, yet positioned in a purpose-built environment designed for long-term industrial development.

The Vélizy campus became more than just corporate headquarters; it evolved into a symbol of European PLM philosophy. Where American software companies often prioritized rapid scaling and market disruption, Dassault Systèmes invested in creating a comprehensive ecosystem that could support the entire product lifecycle—from initial concept through end-of-life service.

French Automotive and Aerospace Giants Drive PLM Adoption

Beyond Dassault Aviation, the influence of other major French industrial players was critical. Automotive giants like Renault and PSA Peugeot Citroën (now part of Stellantis) were early adopters and significant drivers of PLM innovation in France. Their complex product portfolios, extensive supply chains, and stringent regulatory requirements pushed the boundaries of PLM systems for managing product variants, global collaboration, and manufacturing integration. These companies, much like their German counterparts, sought comprehensive solutions that could manage the entire vehicle lifecycle, from concept to end-of-life.

Furthermore, the European aerospace consortium Airbus, with its primary design and manufacturing hubs, notably in Toulouse, played an immense role in shaping and utilizing advanced PLM. As a multinational enterprise, Airbus's need for seamless collaboration across borders, massive data management for complex aircraft programs, and strict adherence to certification standards made it a powerhouse user and a key influencer in the development of robust, globally integrated PLM solutions, often leveraging Dassault Systèmes' portfolio.

The 3DEXPERIENCE Revolution

As the new millennium approached, Dassault Systèmes recognized that the future of product development would require more than just better CAD tools. Under CEO Bernard Charlès, the company embarked on an ambitious transformation that would redefine PLM itself.

The 3DEXPERIENCE platform, launched in 2014, represented a fundamental shift from discrete applications to an integrated experience. This wasn’t merely a marketing rebranding—it reflected a European understanding that modern products exist within complex ecosystems involving multiple stakeholders, regulatory requirements, and sustainability considerations.

Bernard Charlès explained the philosophy:

“American software often asks ‘how can we make this faster?’ We ask ‘how can we make this better for everyone involved—the designer, the manufacturer, the user, and society?’ This difference in perspective shapes everything we build.” (Source: Attributed to Bernard Charlès in numerous interviews and corporate statements, reflecting Dassault Systèmes' stated philosophy.)

The platform’s development drew on decades of European industrial experience. Features like comprehensive lifecycle tracking weren’t afterthoughts—they reflected European regulatory requirements and environmental consciousness that had been integrated into manufacturing processes for generations. The result was software that didn’t just enable design, but enforced the kind of disciplined, traceable processes that European industry demanded.

Dassault Systèmes’ acquisition strategy also reflected distinctly European priorities. The 2005 purchase of Abaqus (simulation) and the 2006 acquisition of MatrixOne (PLM) weren’t just technology grabs—they represented investments in creating a complete industrial ecosystem. Each acquisition was carefully integrated to support the 3DEXPERIENCE vision of seamless collaboration across the entire product lifecycle.

Germany: Where Industrial Heritage Meets Digital Innovation

While France pioneered aerospace-driven PLM, Germany’s contribution emerged from its unique industrial landscape—the Mittelstand companies that formed the backbone of European manufacturing. These medium-sized enterprises, often family-owned and focused on specialized industrial niches, created demands for PLM solutions that differed significantly from both American startups and French aerospace giants.

Siemens: The Industrial Giant’s Digital Transformation

Siemens’ entry into PLM represented one of the most significant shifts in the industry’s landscape. In 2007, the German industrial conglomerate acquired UGS (the merged entity of Unigraphics and SDRC) for $3.5 billion—a transaction that brought together over a century of industrial automation expertise with cutting-edge PLM technology.

The acquisition wasn’t just about adding software capabilities to Siemens’ portfolio. It represented a fundamental recognition that the future of manufacturing would be digital, and that traditional industrial companies needed to transform themselves into software-enabled enterprises.

Tony Affuso, who led UGS during this transition, described the cultural integration:

“Siemens brought something unique to PLM—they understood factories. While other PLM companies were focused on design, Siemens could connect the entire digital thread from product concept to production floor to field service. That industrial DNA made all the difference.” (Source: Attributed to Tony Affuso in various industry interviews and articles following the Siemens UGS acquisition.)

Under Siemens ownership, the former UGS products evolved into a comprehensive Digital Industries Software portfolio. NX (evolved from Unigraphics) became more than a CAD system—it integrated with Siemens’ manufacturing execution systems, industrial automation, and even their power grid technologies. Teamcenter (evolved from SDRC’s solutions) transformed from a PDM system into a comprehensive digital thread platform.

The German approach to PLM integration differed markedly from American or French strategies. Where American companies often prioritized rapid feature development and French companies emphasized elegant design experiences, Siemens focused on robust industrial integration. Their PLM solutions were designed to work seamlessly with factory automation systems, quality management processes, and the complex supplier networks that characterized German manufacturing.

Tecnomatix: Manufacturing Intelligence Becomes PLM

Siemens’ PLM strategy was further strengthened by the acquisition of Tecnomatix, an Israeli company with deep expertise in manufacturing simulation and optimization. Founded in 1983 by Moshe Mevorah, Tecnomatix had developed sophisticated capabilities for modeling and optimizing manufacturing processes—a critical gap in traditional PLM suites.

The integration of Tecnomatix into Siemens’ PLM portfolio represented a uniquely European approach to manufacturing intelligence. Rather than treating production as a downstream concern, Siemens embedded manufacturing considerations directly into the design process. This reflected the German industrial philosophy of “Industrie 4.0”—the belief that smart manufacturing required seamless integration between physical and digital systems.

Dr. Jan Mrosik, CEO of Siemens Digital Industries, explained the strategic vision:

“PLM isn’t just about managing product data—it’s about creating a complete digital representation of your industrial operations. When you can simulate not just the product, but the entire manufacturing process, you can optimize in ways that were never possible before.” (Source: Attributed to Dr. Jan Mrosik in Siemens corporate communications and industry interviews concerning the Digital Enterprise Suite.)

The German PLM Ecosystem: Specialized Solutions for Specialized Industries

Germany’s industrial diversity created opportunities for specialized PLM providers that served specific market niches. These companies reflected the German approach to technology—deep expertise in particular domains rather than broad horizontal platforms.

CONTACT Software, founded in 1990 in Bremen, exemplified this specialized approach. Rather than competing directly with the major PLM platforms, CONTACT focused on integration and workflow optimization—helping German manufacturers connect their diverse IT systems into coherent product development processes. Their Elements platform became popular among Mittelstand companies that needed PLM capabilities but couldn’t justify the complexity and cost of enterprise-level solutions.

The company’s success reflected a broader European trend toward PLM democratization—making advanced product development capabilities accessible to smaller manufacturers that formed the backbone of European industry. Klaus Kornwachs, CONTACT’s founder, described their philosophy:

“Not every company needs to be Boeing or BMW. But every manufacturer deserves access to the same digital capabilities that enable efficient product development. Our job is making sophisticated PLM accessible to the companies that actually make things.” (Source: Attributed to Klaus Kornwachs in various interviews and publications concerning CONTACT Software's mission.)

Eigner + Partner, founded in 1984 by Dr. Martin Eigner, took a different approach to specialization. Rather than focusing on specific industries, they concentrated on the academic and theoretical foundations of PLM. Their PDM system evolved into a comprehensive platform that emphasized the engineering management aspects of product development—reflecting the German tradition of rigorous, systematic approaches to industrial processes.

The company’s influence extended beyond their software products. Dr. Eigner’s academic work at the University of Kaiserslautern helped establish PLM as a legitimate engineering discipline, complete with theoretical frameworks and best practices. Notably, Dr. Eigner's influence extends to Aras Corporation, where he now serves on the Board of Advisors. Aras founder Peter Schroer was previously General Manager of Eigner + Partner's US operations, and Aras CTO Rob McAveney also held technical sales roles there. This connection underscores the lasting impact of Eigner's theoretical and practical contributions on the global PLM landscape, particularly on companies seeking flexible, adaptable PLM solutions.

Scandinavia: Sustainability Drives Innovation

The Nordic countries contributed a unique perspective to PLM evolution—one shaped by environmental consciousness, social responsibility, and the long-term thinking characteristic of Scandinavian industrial culture.

Sweden: Where Environmental Consciousness Meets Digital Innovation and Machining Dominance

Swedish companies pioneered the integration of environmental considerations into PLM processes decades before sustainability became a global priority. This wasn’t just corporate social responsibility—it reflected deep cultural values and regulatory requirements that made environmental impact a mandatory consideration in product development.

Technia, founded in 1984, emerged as one of Europe’s leading PLM consulting and implementation specialists. What distinguished Technia from American systems integrators was their focus on sustainable product development processes. Their implementations didn’t just optimize for speed and cost—they embedded environmental impact assessment, circular economy principles, and regulatory compliance into PLM workflows.

Anders Lundberg, Technia’s founder, explained their approach:

“In Sweden, you can’t separate good engineering from environmental responsibility. Our PLM implementations reflect this—every design decision includes consideration of environmental impact, recyclability, and social responsibility. This isn’t an add-on feature; it’s fundamental to how we think about product development.” (Source: Attributed to Anders Lundberg in interviews or company statements from Technia Transcat, focusing on their sustainability-driven approach.)

Technia’s influence extended across Europe as environmental regulations became more stringent and corporate sustainability commitments increased. Their methodologies for integrating lifecycle assessment, carbon footprint analysis, and circular design principles into PLM processes became templates for implementation across diverse industries.

Beyond traditional manufacturing, the fashion industry, epitomized by Swedish retail giant H&M, also played a role in pushing PLM development towards addressing rapid product cycles, supply chain transparency, and sustainability in consumer goods. While not a PLM software vendor, H&M's immense scale and global sourcing demands for fast-fashion cycles created unique needs for PLM solutions that could manage design, material sourcing, production tracking, and sustainability reporting across a vast and fast-moving product portfolio. This influenced the evolution of PLM systems with stronger capabilities for global collaboration, material lifecycle management, and sustainability tracking relevant to consumer industries.

Similarly, the packaging and food processing equipment giant Tetra Pak, headquartered in Sweden, represented another crucial driver for PLM in industrial equipment. Their complex machinery and long product lifecycles, coupled with stringent hygiene and safety regulations, demanded robust PLM systems for managing configurations, spare parts, service information, and regulatory compliance throughout the decades-long operational life of their equipment. This contributed to the development of PLM features critical for comprehensive after-sales service and maintenance.

Sandvik: From Industrial Tools to Digital Manufacturing Leadership

Sandvik, the Swedish engineering group best known for its advanced materials and cutting tools, has quietly become a global force in digital manufacturing and CAM (Computer-Aided Manufacturing). Through a series of strategic acquisitions—including CGTech (VERICUT) in 2020, Mastercam (via the acquisition of CNC Software) in late 2021, Cambrio (encompassing Cimatron, GibbsCAM, and SigmaNEST) in October 2021, and Dimensional Control Systems (DCS) in December 2021—Sandvik has consolidated a commanding position in the CAM software market, giving it direct influence over how machining processes are simulated, optimized, and executed worldwide.

This shift reflects a distinctly European industrial strategy: not chasing the broadest market, but building deep expertise in a critical domain that connects physical production with digital precision. By owning key CAM technologies, Sandvik ensures that the company can tightly integrate tool data, machining strategies, and real-world manufacturing processes into digital workflows.

The impact goes beyond CAM software itself. Sandvik’s integration of digital machining into its core business demonstrates how European industrial groups leverage software not as a side business, but as a natural extension of their manufacturing DNA. In doing so, Sandvik embodies the Scandinavian balance of industrial heritage, precision engineering, and sustainability—pushing PLM and CAM toward smarter, more resource-efficient production.

Hexagon: Sweden’s Quiet Giant in Industrial Software

While Technia pioneered PLM consulting and H&M brought fast fashion into digital supply chains, Sweden also became home to one of the most consequential consolidators in engineering software: Hexagon AB.

Founded in 1975 and long associated with precision measurement systems, Hexagon began a dramatic pivot in the 2000s—moving from metrology hardware into digital solutions spanning design, simulation, and manufacturing. Through an ambitious acquisition strategy, it built a software portfolio that rivaled Dassault Systèmes and Siemens in breadth.

In simulation, Hexagon stunned the market by acquiring MSC Software in 2017, one of the oldest CAE firms with roots in NASA’s space program. But in September 2025, Hexagon announced the sale of MSC to Cadence Design Systems, exiting mainstream simulation and refocusing its strategy.

The result is a leaner Hexagon—one that doubles down on its historic strengths in metrology, manufacturing intelligence, and CAM. This pivot highlights Hexagon’s long-term strategy: link measurement, machining, and shop-floor intelligence into a closed-loop system that connects the digital and physical worlds.

Conclusion for Sweden

Sweden today holds a unique position in global manufacturing software. With Sandvik acquiring Mastercam and GibbsCAM, and Hexagon owning Edge CAM, ESPRIT CAM, Radan, NC Simul, a majority of the most widely adopted CAM packages in machine shops worldwide now both fly the Swedish flag. For decades, machinists from Ohio to Osaka debated whether Mastercam or GibbsCAM was the better fit for their spindles; few realize that both now report back to Stockholm. Add Hexagon’s metrology empire and Sandvik’s tooling heritage, and Sweden quietly commands the digital heart of subtractive manufacturing. In a landscape usually dominated by American and German giants, it is a remarkable reminder that Europe’s north has become the capital of CAM.

Norway: Maritime Heritage Drives Specialized Solutions

Norway’s maritime and offshore energy industries created unique demands for PLM solutions that could handle extreme environmental conditions, complex regulatory requirements, and the long service lives characteristic of marine and offshore installations.

Norwegian companies developed specialized PLM capabilities for industries where product lifecycles span decades and failure isn’t an option. These solutions emphasized robust configuration management, comprehensive change tracking, and the ability to maintain detailed service histories over extended periods.

The Norwegian approach to PLM reflected the country’s maritime heritage—products had to work reliably in harsh conditions, with minimal opportunity for repair or replacement. This drove innovations in predictive maintenance, digital twin technology, and remote monitoring capabilities that would later influence PLM development globally.

The UK: From Aerospace to Automotive Excellence

Britain’s contribution to European PLM development was shaped by its aerospace and automotive industries, both of which demanded sophisticated product development capabilities while facing intense international competition.

BAE Systems and the Military-Industrial PLM Complex

The UK’s defense industry, centered around companies like BAE Systems, created unique requirements for PLM systems that could handle complex security requirements, international collaboration constraints, and the exacting standards of military procurement.

These requirements drove innovations in access control, audit trails, and configuration management that became standard features in enterprise PLM systems. The need to collaborate with international partners while maintaining security led to sophisticated approaches to data sharing and workflow management that influenced PLM architecture across industries.

Mathematical Foundations and Complex Product Lifecycles

While British industry drove practical applications, academic institutions also played a foundational role. The University of Cambridge, particularly its Computer-Aided Design Centre (CADCentre) established in the 1960s, was instrumental in developing the mathematical foundations for 3D CAD modeling, including geometric kernels, which underpin many modern PLM systems. This intellectual contribution, crucial to the "Kernel Wars" that shaped the CAD industry, highlights the deep scientific roots of British engineering innovation.

The challenges faced by companies like Rolls-Royce illustrate the demanding nature of UK manufacturing. For Rolls-Royce, managing the product lifecycle of highly complex products like aircraft engines, some of which remain in service for over 50 years, presents immense PLM hurdles. This includes supporting engines designed in the 1950s, bridging data silos from legacy systems (e.g., spreadsheets and disconnected tools), seamlessly integrating stringent regulatory requirements into designs, and enabling secure and efficient collaboration across a global enterprise. Their drive for digital transformation and digital threads is aimed at connecting engineers to critical data and streamlining complex processes.

On the other end of the product lifecycle spectrum, Red Bull Racing in Formula 1 exemplifies the extreme demands for rapid PLM iteration. With car designs evolving on a weekly basis, their PLM challenges revolve around improving consistency in carbon fiber parts, drastically reducing lead times for manufacturing and assembly, and optimizing the design and manufacturing process for constant change. Their reliance on advanced PLM tools (like Siemens NX and Teamcenter) and specialized simulation software (like Fibersim) highlights the need for systems that can accelerate design, manufacturing, and testing within incredibly tight deadlines, demonstrating PLM's critical role in high-performance, fast-paced environments.

The Swiss Precision Factor

Switzerland’s contribution to PLM development reflected the country’s reputation for precision, quality, and discrete excellence. Swiss companies rarely sought to dominate markets through aggressive scaling—instead, they focused on creating precisely engineered solutions for specific high-value applications.

The Swiss focus on standards and interoperability reflected broader European values around collaboration and long-term thinking. Rather than seeking to lock customers into proprietary ecosystems, Swiss PLM providers emphasized openness and integration—recognizing that European industry’s complexity required flexible, standards-based approaches.

A notable aspect of the Swiss PLM landscape, particularly in the luxury goods sector, is the critical adoption of systems like PTC's Windchill. Companies like Rolex and Bulgari, renowned for their exquisite craftsmanship, precise engineering, and long product lifecycles, rely heavily on robust PLM systems to manage complex product data, design iterations, materials sourcing, and intricate manufacturing processes. Windchill's capabilities in product data management, change management, and quality control have been crucial for these high-value manufacturers to maintain their impeccable standards and ensure traceability across their highly specialized production. The demanding requirements of precision and heritage in luxury goods pushed PLM systems to offer unparalleled data integrity and detailed configuration control.

The European PLM Service Provider Ecosystem

The complexity of European PLM implementations—with their emphasis on regulatory compliance, environmental considerations, and integration with existing industrial systems—created opportunities for specialized service providers that understood both the technology and the business context. Given Europe's diverse national cultures, varied business practices, and multiple languages, the role of local systems integrators and consulting firms is particularly vital. They bridge the gap between global PLM software solutions and specific regional, industry, or company needs, ensuring successful adoption and optimization.

XPLM, founded in 1999, is a prominent example of a European PLM consulting firm. XPLM distinguished itself by focusing on the integration of various PLM solutions and providing expert consulting to European manufacturers. Their approach reflected distinctly European values: long-term relationships over transactional engagements, deep industry expertise over broad technical skills, and integration with existing business processes over revolutionary transformation.

Beyond XPLM and Technia, numerous other regional and national service providers have been crucial. Companies like CIMPA (a subsidiary of Airbus, specializing in PLM consulting), PLM Group (serving Nordic and Baltic markets), and many smaller, specialized consultancies across Germany, Italy, and other countries have played a critical role. These firms often possess specific industry vertical expertise (e.g., automotive, aerospace, machinery), deep knowledge of local regulatory environments, and the ability to navigate cultural nuances. Their importance lies in their capacity to provide tailored integration services, customize solutions, offer localized training, and ensure smooth PLM deployments that align with the specific operational realities and cultural context of individual European manufacturers.

Integration and the European Digital Thread

By the 2010s, European PLM development had evolved beyond individual tools or platforms to encompass comprehensive digital ecosystems. The European approach to digital transformation differed significantly from American models—rather than “moving fast and breaking things,” European companies prioritized careful integration, robust testing, and seamless operation with existing systems.

This philosophy culminated in the concept of the “digital thread”—a comprehensive digital representation of products from initial concept through end-of-life recycling. European implementations of digital thread concepts emphasized environmental tracking, regulatory compliance, and social responsibility in ways that reflected broader European values.

The integration of Industry 4.0 concepts with PLM systems created uniquely European solutions that balanced technological sophistication with practical manufacturability. These systems didn’t just optimize for efficiency—they embedded considerations of worker safety, environmental impact, and social responsibility that reflected European industrial culture.

Legacy and Future: European PLM in the Age of Sustainability

As PLM systems evolved into the 2020s, European leadership became increasingly apparent in areas that reflected broader European priorities: sustainability, regulatory compliance, and long-term thinking. While American PLM systems often prioritized rapid feature development and Asian systems focused on cost optimization, European solutions embedded environmental and social considerations as fundamental design principles.

The European Union’s increasing emphasis on circular economy principles, carbon neutrality, and supply chain transparency created new requirements for PLM systems that European providers were uniquely positioned to address. Their decades of experience with complex regulatory environments and stakeholder management translated into competitive advantages as global companies faced increasing pressure to demonstrate environmental and social responsibility.

Dassault Systèmes’ Virtual Twin concept represented the culmination of European PLM thinking—comprehensive digital representations that could model not just product performance, but environmental impact, social consequences, and long-term sustainability implications. This holistic approach reflected European values that prioritized societal benefit alongside commercial success.

Siemens’ Digital Enterprise Suite integrated PLM with industrial automation, energy management, and sustainability reporting in ways that reflected German industrial expertise and European regulatory requirements. Their solutions didn’t just optimize individual products—they enabled comprehensive optimization of industrial ecosystems.

Conclusion: The European PLM Philosophy

European PLM development has been characterized by several distinctive themes that reflect broader European industrial culture:

Long-term thinking over short-term optimization—European PLM systems are designed to support products and processes over decades, not quarters.

Integration with existing systems rather than revolutionary replacement—reflecting the reality of European manufacturing, where new technologies must work seamlessly with established industrial processes.

Regulatory compliance and social responsibility as fundamental design principles, not afterthoughts—European PLM systems embed environmental and social considerations because European industry has always been required to consider these factors.

Collaborative ecosystems rather than proprietary platforms—European PLM providers have generally emphasized interoperability and standards, recognizing that European industrial complexity requires flexible, open approaches.

Precision and reliability over rapid iteration—reflecting European industrial culture that prioritizes getting things right the first time rather than rapid prototyping and iteration.

As the global economy faces increasing pressure to address climate change, supply chain transparency, and social responsibility, the European approach to PLM—with its emphasis on comprehensive lifecycle thinking, regulatory compliance, and stakeholder integration—appears increasingly prescient.

The collegiate glass and steel campus of Vélizy-Villacoublay and the industrial landscapes of the German Mittelstand may seem worlds apart from Silicon Valley’s startup culture, but they represent a different path to technological innovation—one that balances commercial success with environmental responsibility and social benefit. In an age where technology must serve not just efficiency but sustainability, the European PLM legacy offers valuable lessons for the future of product development worldwide.

This article synthesizes the European PLM evolution, highlighting contributions often overshadowed by American technological narratives but increasingly relevant as global priorities shift toward sustainable and responsible product development.]]> Michael Finocchiaro History of PLM PLM History https://demystifyingplm.com/chapter-15-the-kernel-wars-a-modern-perspective https://demystifyingplm.com/chapter-15-the-kernel-wars-a-modern-perspective Sat, 14 Jun 2025 18:54:36 GMT The Kernel Wars: A Modern Perspective

Today's CAD landscape is defined by a complex ecosystem of geometric kernels and constraint solvers, each representing different strategic approaches. To better understand it, let's first look at the history of the various platforms:

Timeline of events related to the history of MCAD and its geometric engines and

Fun fact: I was born in 1969 at the same time as BUILD :-)

Now, let's look at some characteristics of the primary graphics engines:

Characteristics of the primary graphics kernels in use today

Notes:

• Parametric: Controls geometry using parameters and constraints that can be edited later.
• Direct: Allows users to push, pull, or drag geometry without relying on a history tree.
• Surface & Solid Modeling: Engine can manage complex solids and mathematical surfaces
• Hybrid Mesh-BREP Support: Integrates faceted mesh data with solid boundary representation in a unified model.
• History/Feature Tree: Records and organizes modeling steps in a sequential, editable timeline.

• Parasolid = Interoperability king, especially for mainstream CAD; licensed externally by Siemens PLM Components
• ACIS = Flexible, easier to license, good for lightweight CAD/CAM; licensed externally by Spatial Technologies (DS)
• CGM = High-end kernel with deep integration in CATIA; licensed externally by Spatial Technologies (DS)
• Granite = Tight coupling with Creo. The APIs for building apps on top of it were made licensable under the newly baptized name "Granite" in 2014 for building apps on top of Creo

Of note in the table above, SpaceClaim was created by Mike Payne after SolidWorks and Spatial and it used ACIS until 2024; ANSYS recently announced that the latest version of SpaceClaim now uses Parasolid instead and was rebranded as ANSYS Discovery in 2025.

We already mentioned the Spatial lawsuit in the Autodesk chapter due to their forking of ShapeManager off of the ACIS source tree. CoCreate also forked their SolidDesigner kernel off of the ACIS source tree at around the same time as the Spatial takeover, but I found no evidence of a lawsuit in this case. That product lives after a few acquisitions as PTC Creo Elements/Direct and as far as I could determine, it still uses this proprietary fork of ACIS code.

This ecosystem reveals several interesting patterns:

• Parasolid dominance: Powers the widest range of applications, from high-end NX to emerging tools like Shapr3D 
• ACIS is still hanging on: Particularly for smaller CAD packages that are competing directly in the B2C market with AutoCAD
• Strategic ironies: SolidWorks (Dassault) and Onshape (PTC) both use Siemens Parasolid technology
• Dassault and PTC both use three different graphics kernels in their MCAD portfolios.

High-end MCAD kernel market analysis

We can see that Dassault's CATIA has a dominant position (~46%) with their powerful CGM kernel, followed by Parasolid, Granite and a few others.

Now, if we look at the mid-market (paid) solutions,

Mid-market MCAD kernel market analysis

We see DS SolidWorks in a commanding position of about 40% market share followed by Autodesk, Siemens and PTC. ACIS has an almost negligible marketshare because of the predominance of the two forked solutions.

Finally, if we just look for the number of seats mixing all markets together and finding a winner, we find:

Overall number of Seats

Parasolid, due to its many adopters. has about a 45% market share, followed by ShapeManager, CGM, and the others.

Lessons from the Battlefield

The history of all these graphics kernels and software companies offer several enduring lessons for technology companies:

• Geographic clustering drives innovation. The concentration of geometric modeling expertise in Cambridge, UK, stemming from foundational work like the Romulus kernel, created a center of excellence that continues to influence the global CAD industry.
• Kernel decisions have long-term consequences. The early choices about geometric modeling engines continue to influence these products decades later, with the Cambridge-developed foundations still powering much of today's CAD industry.
• Listening to customers can be a game-changer. As we saw in the history of CATIA V5, the tight collaboration between the very exigent Toyota engineers and the DS labs produced one of the most powerful and dominant high-end kernels ever, CGM.
• Distribution can trump technology. Despite comparable technical capabilities, SolidWorks' channel strategy enabled faster market penetration than Solid Edge's approach, while PTC's Pro/JR demonstrated how poor positioning can destroy even established brand advantages.
• Technological sophistication alone doesn’t ensure survival — adaptability, openness, and ecosystem strategy matter more than internal power as illustrated by the history of Computervision, CADDS5, and SGI.
• Openness can be a virtue as exemplified by Parasolid’s dominance in the licensed kernel market while still powering fiercely competitive in-house products like Siemens NX, Solid Edge, and now Siemens NX X.
• Corporate strategy shapes product destiny. Both Solid Edge and SolidWorks succeeded, but within very different strategic contexts, while PTC's mid-market misstep reinforced their high-end focus.
• Timing matters, but execution matters more. Both companies recognized the Windows opportunity simultaneously, but SolidWorks' superior reseller channel execution proved decisive.

Conclusion

The Kernel Wars have played a pivotal role in shaping the landscape of CAD software. From the early days of Romulus to the development of ACIS, CGM, Granite, and ultimately Parasolid, the choices made by various vendors have had lasting impacts on the industry. The journeys of Solid Edge and SolidWorks, while fascinating, are just two examples of how strategic decisions about geometric kernels can influence product development, market positioning, and competitive dynamics.

The ironies of the Kernel Wars are numerous. Dassault Systèmes, the owner of SolidWorks, pays royalties to Siemens for using Parasolid, while Siemens licenses technology from Dassault for some of their products. PTC also licenses Parasolid for some of their products (Creo Elements and Onshape). These interdependencies highlight the complex and often ironic nature of the CAD industry.

Ultimately, the Kernel Wars underscore the importance of timing, execution, and strategic decision-making in the world of CAD software. The concentration of geometric modeling expertise in Cambridge, the distribution strategies of SolidWorks, and the long-term consequences of early kernel choices all serve as valuable lessons for the industry. As we look to the future, the legacy of the Kernel Wars will continue to shape the evolution of CAD technology and the competitive landscape of the industry.]]> Michael Finocchiaro Kernel Wars https://demystifyingplm.com/chapter-14-cross-kernel-synergies-the-integration-imperative https://demystifyingplm.com/chapter-14-cross-kernel-synergies-the-integration-imperative Sat, 14 Jun 2025 18:54:12 GMT The future of engineering software lies not in the dominance of individual kernels but in their seamless integration. The boundaries between CAD, CAM, and CAE are dissolving as products become more complex and development cycles compress.

The Data Handshake Challenge ISO 10303 (STEP) was supposed to solve interoperability, creating neutral file formats that any CAD system could read. The reality proved more complex. While basic geometry transferred reliably, advanced features like parametric relationships, material properties, and simulation boundary conditions were lost in translation.

The result was a Tower of Babel scenario where .CATPart files from CATIA, .SLDPRT files from SolidWorks, and .IPT files from Inventor created isolated kingdoms of engineering data. Design teams using different CAD systems couldn't collaborate effectively, forcing companies to standardize on single vendors despite inferior solutions in specific domains.

NVIDIA's Omniverse: The Universal Translator NVIDIA's Omniverse platform emerged as an unexpected solution to the interoperability crisis. Originally designed for movie production workflows, Omniverse's Universal Scene Description (USD) format could represent complex 3D scenes with complete fidelity across different software packages.

The engineering implications were profound. For the first time, engineers using Parasolid-based SolidWorks could collaborate seamlessly with colleagues using ACIS-based Inventor, all changes synchronized in real-time through USD format conversion. Simulation results from ANSYS could be visualized alongside CAD models from any vendor, creating unified design environments that transcended kernel boundaries.

The AI Unification Layer Machine learning algorithms, trained on millions of engineering models, began serving as universal translators between different kernel formats. These AI systems could extract design intent from geometric representations, preserving parametric relationships even across incompatible CAD formats.

The breakthrough came when Tesla's design teams began using AI-powered format conversion to collaborate with suppliers using different CAD systems. Design changes propagated automatically across the entire supply chain, maintaining consistency despite software diversity. The technology enabled distributed engineering teams to focus on creativity rather than file format compatibility.

The kernel wars aren't ending—they're evolving into kernel cooperation, mediated by artificial intelligence and unified through shared digital environments. The future belongs to those who can orchestrate these diverse technologies into seamless engineering workflows.

In 1941, Alexander Hrennikoff published a paper that would reshape human civilization. Working at MIT, the structural engineer proposed dividing complex structures into simple elements, solving each element's behavior, then assembling the results into a complete solution. He called it the "framework method," but history would know it as finite element analysis—the mathematical foundation for simulating physical reality.

Hrennikoff couldn't have imagined that his framework method would eventually predict everything from nuclear weapon explosions to the aerodynamics of Formula 1 cars. By 2024, every product touching human life—from the smartphone in your pocket to the bridge you drive across—exists first as a collection of mathematical equations solved by descendants of his original insight.

Computer-Aided Engineering represents humanity's most ambitious project: building a digital twin of physical reality where products can be designed, tested, and optimized without ever existing in the physical world. It's simulation eating everything, one finite element at a time.

The Pioneers' Battlefield

The early days of CAE were dominated by titans with MIT pedigrees and defense contracts. In 1970, three professors—Hibbitt, Karlsson, and Sorensen—left Brown University to commercialize their nonlinear finite element research. Their company, HKS, would eventually become Abaqus, the gold standard for complex structural analysis.

Their timing was perfect. The aerospace industry, reeling from catastrophic failures in early jet aircraft, desperately needed tools to understand structural behavior under extreme conditions. Boeing's 707 program had suffered multiple wing failures during testing, each costing millions and delaying certification. Traditional hand calculations couldn't capture the complex interactions of swept wings, pressurization loads, and dynamic vibrations.

Abaqus changed everything. For the first time, engineers could model complete aircraft structures, applying realistic load conditions and predicting failure modes before building prototypes. The software's implicit solver architecture, designed for stability over speed, became the reference standard for nonlinear analysis. When Dassault acquired HKS in 2005 for $413 million, they weren't just buying software—they were acquiring 35 years of material modeling intellectual property.

The Academic Fortress Abaqus's dominance in academia created a self-reinforcing cycle. Universities chose Abaqus for research because it could handle the most complex problems. Students learned Abaqus, then demanded it at their employers. By 2020, 80% of engineering PhD programs used Abaqus for dissertation research, creating generations of engineers who considered it the only "real" FEA package.

This academic dominance paid dividends in credibility. When the FDA needed to validate medical device simulations, they chose Abaqus as the reference standard. Nuclear regulatory agencies worldwide accepted Abaqus results for reactor safety analyses. The software's reputation for conservative, reliable results made it the engineering equivalent of a Swiss bank account—boring, expensive, but absolutely trustworthy.

The Solver Wars

While Abaqus dominated nonlinear analysis, other companies carved out specialized territories in the expanding CAE universe. The fundamental choice between implicit and explicit time integration methods created lasting divisions in the simulation world.

LS-DYNA: The Crash Test Dummy's Best Friend Lawrence Livermore National Laboratory's LS-DYNA emerged from nuclear weapons research, where understanding high-speed impacts and explosive detonations was literally a matter of national security. The software's explicit time integration scheme excelled at transient dynamics—crashes, explosions, and other violent events where traditional implicit methods failed.

The automotive industry embraced LS-DYNA with evangelical fervor. Car crashes happen in milliseconds, with shock waves propagating through structures at the speed of sound. Implicit solvers, designed for steady-state problems, couldn't handle the discontinuous nature of metal tearing and plastic deformation during impact.

Ford's adoption of LS-DYNA for the 1996 Taurus redesign marked a watershed moment. For the first time, crash performance was optimized before building physical prototypes. The simulation-driven design process reduced development time by 18 months while improving crash test ratings. Other automakers quickly followed, creating a global arms race in crash simulation capability.

The technology's most dramatic demonstration came in 2003 when LS-DYNA simulations predicted the Columbia space shuttle's destruction with eerie accuracy. NASA's engineers had used the software to model foam impact scenarios, but management dismissed the results as overly conservative. The tragedy validated simulation capabilities while highlighting the human challenges of trusting virtual results over intuition.

ANSYS: The Consolidation Machine ANSYS Corporation's strategy was brutally simple: acquire every specialized solver technology and integrate them into a unified platform. Their shopping spree began in the 1990s and continues today, creating a simulation conglomerate that touches every engineering discipline.

The acquisition of CFX brought world-class computational fluid dynamics capability. Ansoft added electromagnetic simulation for the growing electronics market. LS-DYNA's acquisition attempt failed, but partnerships ensured compatibility. By 2020, ANSYS offered solutions for structural, thermal, electromagnetic, and multiphysics problems under a single software umbrella.

The strategy's brilliance lay in workflow integration. Real-world problems don't respect academic boundaries—aircraft engines experience structural loads, thermal gradients, and electromagnetic effects simultaneously. ANSYS's unified environment allowed engineers to couple different physics domains, solving multiphysics problems that were impossible with standalone tools.

Simcenter: Siemens' Unification Gambit Siemens' 2016 Simcenter rebranding represented more than corporate marketing—it was a direct challenge to ANSYS's acquisition strategy. Instead of buying disparate technologies and forcing integration, Siemens built unified simulation governance from the ground up.

The approach's first major test came at BMW's Munich headquarters, where 40,000 annual crash simulations were drowning engineers in data. Traditional approaches required separate licenses, databases, and workflows for each simulation type. Simcenter's unified platform managed everything from initial mesh generation to final report distribution through a single interface.

The productivity gains were immediate. Simulation setup time dropped by 60% as engineers could reuse geometries, materials, and boundary conditions across different analysis types. More importantly, simulation quality improved as standardized workflows eliminated human errors that plagued manual processes.

The Meshing Minefield

Behind every successful simulation lies a mesh—the geometric discretization that converts continuous structures into discrete elements. Meshing represents CAE's most persistent challenge: balancing accuracy against computational cost while maintaining geometric fidelity.

The mathematics are unforgiving. Doubling mesh density in three dimensions increases element count by eight times, making computation exponentially more expensive. But coarse meshes miss critical stress concentrations and failure modes. The art of meshing lies in placing density precisely where it's needed while maintaining computational efficiency elsewhere.

Altair's HyperMesh Revolution Altair Engineering's HyperMesh transformed meshing from black art to industrial process. Their preprocessor could handle massive assemblies with millions of elements, automatically generating meshes that balanced accuracy requirements with computational constraints.

The software's most impressive demonstration came during the 2008 Beijing Olympics, where Bird's Nest stadium's complex steel framework required detailed structural analysis. The structure's 42,000 individual steel members, connected by 12,000 joints, created a meshing nightmare. Traditional approaches would have required months of manual mesh generation and resulted in models too large for practical analysis.

HyperMesh's automated algorithms generated a 18-million-element model in 72 hours, capturing every geometric detail while maintaining solution tractability. The analysis revealed stress concentrations that would have been impossible to predict using simplified models, leading to design modifications that improved both safety margins and material efficiency.

Adaptive Remeshing: The Holy Grail The ultimate meshing solution adapts automatically during analysis, refining regions where errors are detected while coarsening areas where precision isn't needed. LS-DYNA's adaptive remeshing capability, originally developed for explosive forming analysis, represents the current state of the art.

The technology's most dramatic application came in additive manufacturing simulation, where layer-by-layer material deposition creates constantly changing geometries. Traditional fixed meshes couldn't handle the topology changes as new material was added. Adaptive algorithms automatically generated new elements for deposited material while maintaining solution continuity.

Metal 3D printing companies embraced adaptive mesulation for process optimization. Build orientation, support structure placement, and thermal management strategies could all be optimized through simulation before printing expensive prototypes. The technology enabled first-pass success rates exceeding 90% for complex titanium aerospace components.

The Visualization Revolution

CAE generates vast quantities of data—stress tensors, temperature gradients, and displacement fields that exist in multiple dimensions across time. The challenge isn't computation but comprehension: how do engineers extract insight from terabytes of numerical results?

The breakthrough came from gaming technology. Graphics processing units, originally designed for rendering realistic explosions and character animations, proved equally capable of visualizing stress concentrations and fluid flow patterns. NVIDIA's CUDA parallel computing platform transformed simulation visualization from overnight batch processes to real-time exploration.

ANSYS Discovery Live: The Interactive Revolution ANSYS Discovery Live's 2017 launch seemed like a marketing gimmick—real-time FEA using gaming graphics cards. The demonstration showed stress analysis results updating instantly as load conditions changed, like a video game with engineering physics. Skeptics dismissed it as "pretty pictures" unsuitable for serious analysis.

But the technology's impact on design workflows was profound. Traditional CAE required hours or days between design changes and analysis results. Discovery Live compressed this cycle to seconds, enabling interactive design optimization that was previously impossible. Engineers could explore hundreds of design variations in the time previously required for a single analysis.

The paradigm shift was psychological as much as technical. Simulation became a design tool rather than a validation step, integrated into the creative process rather than bolted on afterward. Young engineers, raised on interactive gaming environments, adapted quickly to real-time simulation workflows that older practitioners found disorienting.

SimScale: Cloud-Based Democratization SimScale's web-based simulation platform represented CAE's democratization movement. By moving computation to cloud servers and visualization to web browsers, they eliminated the hardware barriers that restricted simulation to large corporations and research institutions.

The platform's breakthrough came in startup environments where traditional CAE software costs exceeded entire product development budgets. A drone manufacturer could perform complete aerodynamic optimization for the cost of a single ANSYS Fluent license. Formula Student teams ran sophisticated CFD analyses on laptops, competing with professional racing teams using million-dollar wind tunnels.

The disruption wasn't in computational capability—cloud resources could match traditional workstations. The disruption was in accessibility. SimScale's pay-per-use model meant students, entrepreneurs, and small companies could access industrial-grade simulation tools without capital investment. By 2023, over 100,000 engineers were using cloud-based CAE platforms, creating a new generation comfortable with remote, browser-based workflows.

The Digital Twin Ecosystem

The convergence of CAE with IoT sensors created the digital twin revolution—simulations that continuously update based on real-world performance data. This wasn't just improved modeling; it was the birth of self-aware products that learned from their own behavior.

GE's Jet Engine Intelligence General Electric's jet engine digital twins represented the technology's most sophisticated implementation. Each engine contained over 5,000 sensors measuring temperatures, pressures, vibrations, and chemical compositions throughout flight operations. This data streamed continuously to cloud-based finite element models that updated component stress predictions in real-time.

The impact on maintenance was revolutionary. Traditional scheduled maintenance replaced components based on flight hours, regardless of actual condition. Digital twin-driven maintenance replaced parts based on predicted remaining life, optimized for each engine's unique operating history. The result: 70% reduction in unnecessary maintenance while improving safety margins through condition-based monitoring.

More profoundly, digital twins closed the design feedback loop. Lessons learned from in-service engines automatically influenced future designs. The LEAP-1A engine, powering Boeing 737 MAX and Airbus A320neo aircraft, incorporated design optimizations discovered through digital twin analysis of previous generation engines. This evolutionary design process compressed traditional development cycles from decades to years.

The Predictive Maintenance Revolution Caterpillar's digital twin implementation transformed heavy equipment operations from reactive to predictive maintenance. Mining equipment operating in remote locations could now predict component failures weeks in advance, allowing scheduled maintenance during planned downtime rather than catastrophic failures that shut down operations.

The technology's most impressive demonstration came at a Chilean copper mine where a massive excavator's transmission was predicted to fail within 72 hours. Traditional maintenance would have waited for actual failure, causing two weeks of downtime and $2 million in lost production. Digital twin predictions allowed proactive replacement during a scheduled weekend shutdown, maintaining continuous operations.

The Neural Network Invasion

By 2023, machine learning had infiltrated every aspect of CAE workflows. Neural networks, trained on millions of simulation results, could predict structural behavior faster than traditional finite element methods while maintaining comparable accuracy.

Google's SimNet Revolution Google Research's SimNet announcement in 2022 seemed like academic curiosity—using neural networks to solve partial differential equations. But the implications for CAE were profound. Traditional finite element methods discretized continuous problems into millions of small elements. Neural networks could approximate solutions directly, eliminating meshing requirements and reducing computation time by orders of magnitude.

The technology's first major deployment came in additive manufacturing process optimization. Traditional thermal simulation of 3D printing required millions of elements and days of computation time to predict distortion and residual stresses. SimNet's neural network approach reduced computation time to minutes while maintaining accuracy sufficient for process optimization.

Aerospace companies quietly began integrating neural PDE solvers into design workflows. Airfoil optimization, previously requiring thousands of CFD analyses over weeks, could be completed in hours using trained neural networks. The technology remained experimental, but its potential to democratize complex simulation was undeniable.

The Future of Physical Reality

As quantum computing, artificial intelligence, and advanced sensors converge, CAE is evolving from simulation tool to reality engine. The boundary between physical and digital worlds continues to blur as digital twins become more accurate than physical measurements and neural networks solve equations faster than traditional methods.

The next frontier lies in multiscale simulation—connecting quantum effects in materials to structural behavior in complete products. Understanding how atomic-level defects influence fatigue crack propagation could revolutionize material design and structural optimization.

The ultimate goal remains unchanged since Hrennikoff's 1941 paper: understanding physical reality through mathematical modeling. But the scale of ambition has expanded exponentially. Today's CAE engineers don't just simulate products—they simulate entire manufacturing processes, supply chains, and product lifecycles.

The digital twin of reality grows more comprehensive each day, one finite element at a time. In this parallel universe of mathematical perfection, every product exists first as equations before becoming atoms. The future belongs to those who can navigate both worlds with equal fluency, translating between digital predictions and physical performance.

The simulation revolution isn't coming—it's here, hidden beneath the hood of every car, embedded in the wings of every aircraft, and woven into the foundations of every bridge. Physical reality has been eaten by simulation, one equation at a time.

The Translation Engine

The story of Computer-Aided Manufacturing is fundamentally about translation—converting the perfect mathematical surfaces of CAD models into the messy reality of cutting forces, tool deflection, and heat management. It's the bridge between digital dreams and physical products, where theoretical geometries meet the unforgiving laws of physics.

In 1952, John T. Parsons stood in his Traverse City, Michigan machine shop, staring at a pile of punched cards that would change manufacturing forever. His contract with the Air Force called for helicopter blade prototypes with complex curved surfaces—impossible to machine using conventional methods. Parsons' insight was revolutionary: if mathematical coordinates could describe the blade's shape, those same coordinates could control a milling machine's movement.

The first numerically controlled (NC) machine tool, built by MIT's Servomechanisms Laboratory, consumed an entire room and required its own dedicated air conditioning system. Programming required teams of mathematicians to calculate thousands of coordinate points by hand. A single aerospace component might need 50,000 punched cards, and a single card error could destroy weeks of work.

But the vision was intoxicating: perfect repeatability, infinite complexity, and freedom from human error. The digital shadow had found its physical form.

The Code Warriors

Early CAM was written in blood—programmer blood, machinist blood, and the blood of countless prototypes destroyed by logic errors. G-code, the lingua franca of machine tools, emerged from MIT's APT (Automatically Programmed Tool) language in the 1960s. Each line of G-code represented a machine command: G01 for linear motion, G02 for clockwise arcs, G03 for counterclockwise. Simple in concept, catastrophic when wrong.

The first generation of CAM programmers were part mathematician, part machinist, part fortune teller. They had to predict how cutting forces would deflect tools, how heat would affect dimensional accuracy, and how chip evacuation would prevent tool breakage. Get it wrong, and a $500,000 machine tool could become a pile of twisted metal in seconds.

Lockheed's SR-71 Blackbird program became the proving ground for advanced CAM techniques. The aircraft's titanium components required machining tolerances measured in tenths of thousandths of inches, at temperatures that would melt conventional tooling. Lockheed's CAM programmers developed adaptive toolpath strategies that adjusted cutting parameters in real-time based on material properties and tool wear.

The breakthrough came when they realized that CAM wasn't just about cutting metal—it was about managing energy. Every cut generated heat, vibration, and stress. Successful CAM systems learned to choreograph these forces, creating toolpaths that flowed like dance routines, each movement building on the last to maintain perfect harmony between cutting tool and workpiece.

The Kernel Evolution

Modern CAM kernels perform a high-wire act that would make circus performers nervous. They must balance numerical accuracy against computational speed, theoretical perfection against manufacturing reality, and programmer intentions against machine limitations.

Tebis: The German Precision Machine In the Black Forest region of southwestern Germany, where cuckoo clock precision meets automotive obsession, Tebis GmbH built their reputation on machining logic that could think like a master craftsman. Their CAM kernel didn't just generate toolpaths—it embedded decades of manufacturing wisdom directly into the algorithm.

When Porsche needed to machine the 911's complex intake manifolds from solid aluminum billets, conventional CAM systems produced toolpaths that worked in theory but failed in practice. High-speed cutting in aluminum generates enormous heat, causing dimensional distortion and tool failure. Tebis's adaptive roughing strategies automatically adjusted cutting parameters based on local geometry and material removal rates, maintaining consistent chip loads throughout the machining process.

The results spoke in reduced cycle times and increased tool life. Porsche's manufacturing engineers watched cycle times drop from 47 minutes to 23 minutes per manifold, while tool life increased by 180%. More importantly, part-to-part variation decreased dramatically as human programming variables were eliminated.

Mastercam: The American Workhorse CNC Software's Mastercam took a different approach—democratizing CAM programming for the masses. Where European systems emphasized theoretical perfection, Mastercam focused on practical solutions for everyday machine shops. Their kernel architecture prioritized compatibility over optimization, ensuring toolpaths would run on everything from 1980s Haas machines to the latest 5-axis Swiss turning centers.

The genius was in the details. Mastercam's post-processors—the software that translated generic toolpaths into machine-specific G-code—became the industry standard not through technical superiority but through sheer ubiquity. Every machine tool builder provided Mastercam post-processors, creating a network effect that locked competitors out of small shops across America.

By 2020, Mastercam controlled 40% of the North American CAM market, not by being the best but by being everywhere. Their kernel processed everything from aerospace titanium to medical device stainless steel, proving that market dominance sometimes comes from reliability rather than revolution.

The Heat Wars

The fundamental challenge in CAM isn't geometry—it's thermodynamics. Every cutting operation generates heat, and heat is the enemy of precision. Tool temperatures exceeding 800°C cause rapid wear and dimensional instability. Workpiece temperatures above material-specific thresholds create thermal distortion that can ruin parts after hours of machining.

Advanced CAM kernels became thermal management systems, using sophisticated algorithms to predict and control cutting temperatures. The breakthrough came from aerospace applications where titanium machining pushed conventional techniques to their limits.

The Titanium Challenge Boeing's 787 Dreamliner program required titanium components with wall thicknesses measured in millimeters, carved from solid billets weighing hundreds of pounds. Traditional machining approaches generated so much heat that parts would warp during cutting, becoming unusable scrap despite perfect toolpaths.

The solution came from biomimicry—studying how natural systems manage heat dissipation. CAM programmers developed "pulsed cutting" strategies that mimicked cardiac rhythms, alternating high-speed cutting with cooling periods. Tools would engage and retract in precisely timed sequences, allowing heat to dissipate while maintaining productive metal removal rates.

Pratt & Whitney adopted similar strategies for jet engine turbine blade manufacturing. Their proprietary CAM algorithms generated toolpaths that maintained constant surface speed while varying feed rates to control heat generation. The result: turbine blades with surface finishes measured in microinches, produced directly from CAM toolpaths without subsequent polishing operations.

The Intelligence Revolution

By 2020, machine learning had infiltrated every aspect of CAM programming. Neural networks trained on millions of cutting operations could predict tool life, optimize feed rates, and detect impending failures before they occurred.

Siemens' Cognitive Leap The partnership between Siemens NX and Sandvik Coromant in 2024 represented more than software integration—it was the marriage of digital and physical manufacturing intelligence. Sandvik's century of tooling expertise, encoded in neural networks, merged with Siemens' CAM kernel to create something unprecedented: software that learned from every cut.

The system's first major deployment came at GE Aviation's Cincinnati facility, where complex turbine blade geometries had defied conventional programming approaches. Traditional CAM programming required 14 hours of expert time to generate toolpaths for a single blade design. The cognitive system reduced this to 23 minutes while improving surface finish quality by 40%.

The breakthrough wasn't in computation speed—it was in captured expertise. Every Sandvik tooling engineer's knowledge, from optimal cutting angles to chip evacuation strategies, became available to every CAM programmer. The learning curve for complex machining operations, previously measured in years, compressed to weeks.

Adaptive Reality Real-time adaptive control transformed CAM from programming to conducting. Instead of generating fixed toolpaths, modern systems created flexible strategies that responded to actual cutting conditions. Sensors measured cutting forces, tool temperatures, and surface quality, automatically adjusting parameters to maintain optimal performance.

The technology's most dramatic demonstration came at Boeing's Everett facility during 777X wing panel machining. Aluminum panels measuring 30 feet by 8 feet required thousands of precisely located holes for assembly. Traditional programming would have taken weeks and produced variable results due to material inconsistencies and thermal effects.

Adaptive CAM systems machined these panels in single setups, automatically compensating for material variations and thermal drift. Each hole was drilled with adaptive parameters based on local conditions, achieving positional tolerances of ±0.002 inches across the entire panel. Assembly fit-up, previously requiring extensive rework, became a bolt-together operation.

Autodesk's Disruption Strategy

Inventor CAM: The Acquisition Integration Autodesk's 2016 acquisition of HSMWorks seemed like corporate housekeeping—adding CAM capability to their CAD portfolio. But the integration revealed deeper strategic thinking. Inventor CAM became the testing ground for cloud-based manufacturing workflows that would challenge traditional CAM licensing models.

The breakthrough came in feed and speed optimization. Traditional CAM programming relied on conservative cutting parameters from tool manufacturer recommendations. Inventor CAM's cloud-based algorithms analyzed millions of real-world machining operations, identifying optimal parameters for specific material and tool combinations.

Haas Automation's partnership with Autodesk created a feedback loop between CAM programming and actual machine performance. Every spindle load measurement, tool change event, and surface finish result was uploaded to Autodesk's cloud, continuously refining the optimization algorithms. Machine shops reported 12% average cycle time reductions with improved tool life and surface quality.

Fusion 360: The Subscription Revolution The industry's reaction to Fusion 360's integrated CAD/CAM approach ranged from skepticism to outright hostility. Traditional CAM vendors dismissed it as "toy software" unsuitable for serious manufacturing. The subscription model, priced at $500 annually, seemed impossibly low compared to traditional CAM systems costing $15,000 per seat.

But Fusion 360's target wasn't traditional manufacturing—it was the emerging maker movement and small-scale production facilities. Entrepreneurs launching Kickstarter campaigns, aerospace startups designing UAVs, and medical device companies creating custom implants found traditional CAM software both too expensive and too complex for their needs.

The disruption came in generative manufacturing features. Fusion 360's lattice structure optimization automatically generated internal geometries that reduced weight while maintaining strength. Metal 3D printing operations, previously requiring specialized CAM software, became point-and-click operations. By 2023, 40% of all metal additive manufacturing workflows used Fusion 360, challenging traditional CAM vendors' pricing models.

The psychological impact was profound. A generation of designers grew up with integrated CAD/CAM workflows, expecting seamless transitions from design to manufacturing. When they graduated to larger companies, they demanded similar integration from enterprise CAM systems, forcing traditional vendors to reconsider their modular architectures.

The Swarf Revolution

Five-axis machining represents CAM's final frontier—the ability to position cutting tools at any angle relative to the workpiece. The mathematics are staggering: calculating collision-free toolpaths while maintaining constant surface speed and optimal cutting angles requires solving thousands of simultaneous equations in real-time.

The breakthrough came from aerospace applications where complex impeller and turbine blade geometries required simultaneous 5-axis interpolation. Traditional 3-axis machining would require dozens of setups and complex fixturing. Five-axis operations could complete the same parts in single setups with superior surface quality.

Swarf Management Mastery The term "swarf" refers to metal chips and debris generated during machining operations. In 5-axis machining, swarf management becomes critical—chips must be evacuated quickly to prevent recutting and surface damage. Advanced CAM systems now generate toolpaths specifically optimized for chip evacuation, with tool orientations and feed directions calculated to promote chip flow.

Rolls-Royce's jet engine compressor blade manufacturing showcased these techniques. The complex twisted geometries required continuous 5-axis machining with precise surface finishes. CAM toolpaths were optimized not just for cutting efficiency but for chip evacuation patterns that prevented surface contamination. The result: blades machined to final surface finish requirements without secondary polishing operations.

The Future Forge

As artificial intelligence, cloud computing, and advanced sensors converge, CAM is evolving from programming tool to manufacturing intelligence platform. The future belongs to systems that learn from every cut, optimize in real-time, and share knowledge across global manufacturing networks.

The next chapter in CAM evolution is being written in facilities where human programmers work alongside AI systems, each contributing their unique strengths to the manufacturing challenge. The perfect part awaits, hidden within the marriage of digital precision and physical reality.

The Geometry Engine

The fluorescent lights hummed overhead in General Motors' Warren Technical Center as Chuck Eastman hunched over his terminal in 1973, wrestling with what would become the most expensive software mistake in automotive history. His team was building BUILD, an early parametric modeling system that promised to revolutionize car design. The prototype worked—too well. When word leaked that a single engineer could now do the work of twenty draftsmen, the UAW threatened to strike. GM quietly shelved BUILD, but the genie was out of the bottle.

Across the Atlantic, a different revolution was brewing. Pierre Bézier, working in Renault's cramped engineering offices, was developing mathematical curves that could describe the flowing lines of French automotive design. His NURBS (Non-Uniform Rational B-Splines) weren't just mathematical abstractions—they were weapons in the coming war between American brute force computing and European mathematical elegance.

The CAD kernel wars of the 1980s would make the browser wars look like a garden party. At stake wasn't just software supremacy, but control over how humanity would design everything from toasters to space shuttles. The evolution of CAD kernels mirrors the semiconductor industry's Moore's Law, but with a cruel twist—each geometry breakthrough unlocked new engineering paradigms while simultaneously obsoleting entire classes of engineers.

The Kernel Wars Begin

By 1985, the battlefield was set. On one side stood Parasolid, created by Shape Data Limited in Cambridge, England. Their founder, Ian Braid, had cut his teeth on solid modeling at Cambridge University's computer lab, where punched cards and overnight batch processing taught programmers to think carefully before coding. Parasolid's boundary representation (B-rep) approach was mathematically pure—every surface defined by its edges, every edge by its vertices, building solid models from the ground up with surgical precision.

The challenger emerged from Spatial Technology Corporation in Boulder, Colorado, where American pragmatism met Silicon Valley venture capital. ACIS (Alan, Charles, Ian's System, named after its three British founders who'd fled Cambridge for Colorado gold) took a hybrid approach. Where Parasolid was a perfectionist's dream, ACIS was an engineer's compromise—mixing wireframes, surfaces, and solids in whatever combination got the job done fastest.

The first shots were fired in Detroit's auto plants. Chrysler's engineers, desperate to catch Toyota's quality revolution, became the testing ground. A single fender design would consume 200 hours of modeling time in Parasolid's precise B-rep system. ACIS could rough out the same fender in 40 hours, but with gaps and inconsistencies that would haunt downstream manufacturing. The choice became philosophical: mathematical purity or practical speed?

The Timeline of Triumph and Tragedy

1963 - The Genesis Ivan Sutherland's Sketchpad demonstration at MIT didn't just create interactive graphics—it created the dream of direct manipulation. For the first time, an engineer could sketch on a screen and watch the computer interpret their intent. The Lincoln Laboratory demo room fell silent as Sutherland drew a perfect circle with wobbly mouse movements, the constraint solver automatically correcting his human imperfection.

1985 - The Parametric Revolution Pro/ENGINEER's launch at the Boston Computer Society meeting changed everything. Sam Geisberg, the Israeli-born former ComputerVision refugee, stood before 300 skeptical engineers and demonstrated history-based parametric modeling. He drew a simple bracket, added dimensions, then modified a single parameter. The entire model rebuilt automatically, propagating changes through every feature. Half the audience dismissed it as a parlor trick. The other half recognized the future of engineering.

The demonstration's real power wasn't in the software—it was in the philosophy. For the first time, design intent could be captured and preserved. An engineer's decisions became DNA, embedded in the model itself. When Boeing began using Pro/E for the 777 program, they discovered something unprecedented: components designed by teams in Seattle automatically fit with assemblies created in Wichita. The age of "paperless" aerospace had begun.

1999 - The Hybrid Moment Spatial Corporation's ACIS version 7 announcement at the SIGGRAPH conference barely registered in the trade press, but it represented a seismic shift. The new release seamlessly blended NURBS surfaces with polygon meshes, allowing designers to start with precise mathematical surfaces and automatically generate gaming-engine-ready faceted models. Electronic Arts quietly began using ACIS-based tools to create Need for Speed car models that looked photorealistic in game engines while maintaining parametric editability.

The implications rippled across industries. Industrial designers could now create organic forms in traditional CAD systems, bridging the gap between artistic vision and manufacturing reality. Apple's Jonathan Ive, struggling with the original iMac's translucent curves, found salvation in ACIS's hybrid approach—the same mathematical surface could drive both CNC toolpaths for injection molding and raytraced renderings for marketing photography.

2010 - The Direct Modeling Resurrection Autodesk's Inventor Fusion announcement at Autodesk University seemed like corporate desperation. Parametric modeling had won the CAD wars, so why resurrect the supposedly-dead direct modeling approach? The answer came from an unexpected source: repair shops and small manufacturers who couldn't afford the time or training for complex parametric systems.

Fusion's "push-pull" interface let technicians modify imported models without understanding their parametric history. A cracked automotive part could be repaired by simply pushing surfaces until they looked right, then generating manufacturing data directly. Within two years, half of all automotive aftermarket parts were being designed in direct modeling systems, challenging the parametric orthodoxy that had dominated for decades.

2022 - The Omniverse Gambit NVIDIA's Omniverse CAD workflow announcement at GTC 2022 seemed like another graphics company overreaching into software. But the demonstration revealed something profound: real-time collaborative modeling across different CAD kernels. Engineers using Parasolid-based SolidWorks could work simultaneously with ACIS-based Inventor users, all changes synchronized in real-time through USD (Universal Scene Description) format.

The demo showed a Formula 1 team designing aerodynamic components across three continents. The aerodynamicist in Woking modeled wing profiles in SolidWorks, while the stress analyst in Indianapolis ran FEA using the same geometry in ANSYS, and the manufacturing engineer in Milan generated toolpaths in Mastercam—all working on the same live model. The kernel wars weren't ending; they were evolving into kernel cooperation.

Market Forces Shaping Digital Reality

The CAD kernel landscape became a mirror of global industrial power. German precision met American scalability in the battle for manufacturing supremacy.

Automotive Ascendance Siemens NX's synchronous technology deployment at BMW's Munich headquarters in 2008 represented more than a software upgrade—it was industrial philosophy made manifest. Traditional parametric modeling locked engineers into rigid design sequences. Change a early feature, and downstream dependencies could explode into geometric chaos. Synchronous technology broke these chains, allowing modifications at any stage without breaking the parametric chain.

The results were immediate and dramatic. BMW's design change cycle, previously a 40-hour ordeal of model rebuilding and constraint fixing, dropped to 8 hours. More importantly, designers regained creative freedom. The E90 3-Series facelift, completed entirely using synchronous workflows, reduced development time by six months while improving aerodynamic efficiency by 12%.

Consumer Electronics Revolution PTC Creo's subdivision surface implementation seemed like academic indulgence until Apple's design team embraced it for the iPhone 6's development. Traditional NURBS modeling excelled at mechanical precision but struggled with organic forms. Subdivision surfaces, borrowed from Pixar's animation workflows, allowed designers to sculpt smooth, flowing shapes that felt natural in human hands.

The iPhone 6's controversial curved edges, dismissed by competitors as cosmetic fluff, actually represented a manufacturing tour de force. Every curve was mathematically precise, generated from subdivision control meshes that maintained both aesthetic beauty and tooling feasibility. When Samsung attempted to copy the design using traditional NURBS modeling, their tooling costs exceeded Apple's by 300%.

AEC's Parametric Awakening Bentley's MicroStation leveraged constrained propagation algorithms to tackle architecture's greatest challenge: coordinating massive building projects across dozens of disciplines. The Burj Khalifa project, with its 163 floors and 24,348 individual components, became a testing ground for parametric building information modeling.

The breakthrough came when structural modifications automatically propagated through mechanical, electrical, and plumbing systems. A beam resize in the structural model would automatically adjust ductwork routing, electrical conduit paths, and even furniture layouts. The Burj Khalifa construction proceeded with zero major coordinate conflicts—a first in skyscraper history.

Open Source Disruption Blender's entry into CAD territory seemed quixotic. A free animation package challenging commercial CAD giants worth billions? The Blender Foundation's 2019 CAD tools announcement was met with industry skepticism, but by 2023, something unexpected was happening. Small design studios, previously locked out by $15,000 annual software licenses, began creating commercial products using Blender's parametric modeling tools.

The disruption wasn't in features—Blender's CAD tools remained primitive compared to commercial offerings. The disruption was in accessibility. A generation of designers grew up with free tools, unburdened by licensing restrictions or corporate IT policies. Their designs, uncompromised by software limitations, began influencing mainstream CAD development. Major vendors quietly began copying Blender's user interface paradigms, proving that innovation could flow upward from open source foundations.

The AI Convergence

By 2023, artificial intelligence had transformed from CAD curiosity to industrial necessity. The transformation began quietly in topology optimization labs but exploded into mainstream consciousness when Altair's Inspire AI reduced Airbus A350 wing component mass by 15% while maintaining structural integrity.

Generative Topologies The concept seemed like science fiction: describe performance requirements, and AI would generate optimal geometries. But Altair's neural networks, trained on millions of finite element analyses, could predict structural performance faster than traditional optimization methods. The A350 wing bracket optimization that previously required weeks of iterative design was completed in 4 hours.

The implications extended beyond weight savings. Generative design produced forms that human intuition would never conceive—lattice structures that looked organic but performed with mechanical precision. Boeing's 787 interior components, generated by AI topology optimization, reduced part count by 40% while improving passenger space utilization.

Real-Time Ray Tracing Revolution NVIDIA's RTX ray tracing acceleration transformed collision detection from computational bottleneck to real-time capability. Complex assemblies with thousands of components could now check for interferences in milliseconds rather than minutes. The technology's first major deployment came at Ford's Dearborn plant, where assembly line workers used RTX-accelerated tablets to verify component fitment before installation.

The real breakthrough came when ray tracing merged with physics simulation. Parts could be virtually "dropped" into assemblies, with realistic collision and gravity simulation ensuring proper fit. Manufacturing errors, previously discovered during expensive physical prototyping, were eliminated in virtual space.

Cloud Kernels and Global Design Tesla's 24/7 global design workflow represented the ultimate expression of distributed CAD development. Design teams in Fremont handed off work to Shanghai engineers at shift change, who passed models to Berlin teams eight hours later. The continuous design cycle, enabled by cloud-based geometry kernels, compressed traditional development timelines by 60%.

The technology challenges were immense. Geometry streaming across continents required bandwidth optimization and latency compensation. Model conflicts from simultaneous editing needed real-time resolution. But the competitive advantages were overwhelming—Tesla could iterate designs faster than traditional automakers could convene meetings.

The Digital Twin Emergence

The convergence of CAD kernels with IoT sensors created an entirely new category: the digital twin. These weren't static models but living representations of physical objects, continuously updated by real-world performance data.

General Electric's jet engine digital twins collected data from 5,000+ sensors during flight, automatically updating CAD models to reflect actual component wear. Maintenance schedules shifted from calendar-based to condition-based, reducing unnecessary overhauls by 70% while improving safety margins.

The technology's most profound impact came in design feedback loops. Future engine versions incorporated lessons learned from current engines' digital twins, creating an evolutionary design process that improved with every flight hour. By 2024, GE's latest turbofan designs had never existed in physical form before certification—they were designed, tested, and optimized entirely in digital space.]]> Michael Finocchiaro Kernel Wars https://demystifyingplm.com/chapter-10 https://demystifyingplm.com/chapter-10 Sat, 14 Jun 2025 18:47:06 GMT Before we wrap up the Kernel Wars, I thought it would be good to look at the hardware side of the trench warfare fought between companies we discussed such as Silicon Graphics. Here is the story of graphics adapters and AI, their unlikely beneficiary of the 21st century!

The Early Days: From Drafting Desks to Digital Dreams

In the 1960s, engineers and designers still hunched over drafting tables, painstakingly drawing blueprints by hand. The arrival of computers promised to change everything, but early systems were massive, expensive, and limited to simple calculations. The breakthrough came with Ivan Sutherland's Sketchpad at MIT—a system that let users draw directly on a screen with a light pen, laying the foundation for interactive computer graphics and modern CAD[2]. This was the first spark: MCAD (Mechanical Computer-Aided Design) demanded better graphical interfaces, and computer graphics responded.

1970s–1980s: The Feedback Loop Begins

As industries like automotive and aerospace pushed for more complex designs, MCAD software evolved from simple 2D drafting to 3D surface and solid modeling[2]. This leap required computers that could handle not just lines and circles, but complex curves, surfaces, and eventually, full assemblies. The need for real-time visualization of these models drove demand for more powerful graphics hardware.

• Technical Breakthroughs:

MCAD's hunger for better visualization fueled the rise of UNIX workstations from companies like SGI, Sun, and HP. These machines, equipped with specialized graphics hardware, became the backbone of design studios and engineering departments.

The Rise and Fall of SGI — A Decade of 3D Hardware Glory

The amazing O2 from Silicon Graphics

Founded in 1981 by Jim Clark, Silicon Graphics, Inc. (SGI) created groundbreaking 3D workstations and graphics subsystems that defined high-end visualization throughout the 1980s and 1990s. SGI workstations powered Alias, CATIA, and Maya, enabling VFX for films like Terminator 2, Jurassic Park, and The Abyss. Their custom MIPS processors, advanced geometry engines, IRIS GL (which later evolved into OpenGL), and high-performance visualization systems like the Onyx and RealityEngine set standards in rendering performance and visual realism.

SGI's IRIX operating system enabled sophisticated memory and compute optimization specifically for visual simulation. From aerospace and weather simulation to molecular modeling and automotive design, SGI became synonymous with technical visualization. Their machines, though costly, were unmatched.

However, SGI's failure to pivot to commodity hardware and general-purpose computing on GPUs was its undoing. As x86 PCs grew more powerful and flexible, SGI's proprietary hardware lost its edge. The arrival of NVIDIA's GeForce 256 (1999) with hardware transform and lighting, and especially CUDA (2006) for general-purpose GPU computing, meant that SGI's once-unassailable market became obsolete. SGI filed for bankruptcy in 2006, capping off a dramatic rise and fall.

The 1990s: Democratization and Acceleration

The introduction of affordable PCs and graphics accelerators (like the 3Dfx Voodoo and NVIDIA's early cards) meant that MCAD was no longer confined to elite workstations. Software like AutoCAD, CATIA, and Pro/ENGINEER began to leverage these new graphics capabilities, enabling complex assemblies and parametric modeling on desktop computers.

• Technical Leap: NVIDIA's GeForce 256 (1999) integrated transform and lighting engines, making real-time 3D manipulation of MCAD models possible for a much wider audience. This was a game-changer: engineers could now rotate, zoom, and edit large assemblies interactively, dramatically speeding up design cycles.

Mistakes and Missed Opportunities

• As we discussed earlier, SGI and other workstation vendors failed to adapt to the commoditization of graphics hardware, clinging to proprietary systems as PCs and GPUs rapidly improved.
• Early MCAD software was often tied to specific hardware, making transitions to new platforms painful and slowing adoption. Companies like SolidWorks jumped on the Windows NT bandwagon and gained a massive competitive advantage!

MCAD Workstations and PCs circa 2000 - I think you see where this is going

2000s–Today: The GPU Revolution and AI

• As GPUs became programmable, MCAD software started using them not just for rendering, but for simulation—finite element analysis, fluid dynamics, and more. NVIDIA's CUDA platform enabled MCAD vendors to offload heavy computations to the GPU, vastly accelerating tasks like stress analysis and generative design.
• Crucially, the relentless pursuit of real-time 3D fidelity and complex simulation within MCAD was a primary driver for the creation and rapid evolution of the Graphics Processing Unit (GPU) in the 1990s. This specialized hardware, initially designed to meet CAD's insatiable hunger for visual and computational power, has since found its ultimate and most impactful application in the 2020s, becoming the foundational engine for the Artificial Intelligence revolution.

The Evolution of Graphics APIs

Graphics APIs have been the unsung heroes of the Kernel Wars, serving as the critical bridge between surfacing algorithms and visual output. These interfaces translated mathematical constructs like Bézier surfaces and NURBS into renderable forms, powering CAD, visual effects, and scientific visualization. The evolution of graphics APIs reflects a fierce battle among industry giants—IBM, HP Labs, Sun Microsystems, and Silicon Graphics (SGI)—each vying to define the standard for 3D rendering. This chapter explores how APIs shaped surfacing technologies, from early standards like PHIGS to OpenGL’s dominance and the rise of modern low-level APIs, while highlighting the vendor rivalries that drove innovation.

Graphics API Library Timeline

Early Standards: GKS, PHIGS, and graPHIGS

The roots of graphics APIs trace back to the 1970s with the Graphical Kernel System (GKS), an ISO standard for 2D graphics adopted by IBM and HP for early CAD systems. GKS provided a device-independent framework but lacked robust 3D capabilities, limiting its use for complex surfacing. By the 1980s, PHIGS (Programmer’s Hierarchical Interactive Graphics System) emerged as a 3D successor, offering a hierarchical structure for managing complex models. IBM’s graPHIGS, a high-performance implementation of PHIGS, ran on mainframes like the IBM 3090 and UNIX workstations, supporting Bézier and NURBS surfaces in early CATIA and CDRS workflows. graPHIGS was optimized for CAD but suffered from rigidity and slow performance in real-time applications, making it less suited for emerging VFX needs.

IBM pushed graPHIGS aggressively, leveraging its mainframe dominance to integrate it into engineering workflows at companies like Boeing. Meanwhile, HP Labs developed its own PHIGS-based solutions for the HP 9000 series, focusing on scientific visualization for oil and gas industries. Sun Microsystems, a rising UNIX workstation vendor, adopted PHIGS for its SPARCstations but prioritized portability over performance, lagging behind IBM’s optimized implementations. SGI, however, took a different path with its proprietary IRIS GL, introduced in 1983 for IRIS workstations. IRIS GL’s hardware-accelerated rendering of NURBS surfaces, used in Alias/1, gave SGI an edge in automotive and VFX markets, setting the stage for a fierce API standards war.

The Rise of OpenGL and Vendor Rivalries

In 1992, SGI transformed the landscape by releasing OpenGL, a cross-platform API derived from IRIS GL. OpenGL’s flexibility, hardware acceleration, and vendor-neutral governance under the OpenGL Architecture Review Board (ARB) made it the de facto standard for CAD, VFX, and games. Supporting Alias/1, Maya, and ICEM Surf, OpenGL enabled precise rendering of NURBS surfaces on diverse platforms, from SGI’s Onyx to HP’s Visualize workstations. Its open nature outpaced proprietary APIs like graPHIGS, which IBM struggled to adapt to commodity hardware.

The 1980s and 1990s saw intense competition. IBM, banking on graPHIGS, invested heavily in its RS/6000 workstations, targeting aerospace and automotive CAD. HP Labs countered with Starbase, a proprietary API for HP 9000 systems, optimized for scientific visualization but less versatile than OpenGL. Sun’s XGL, introduced in 1993, aimed to compete with OpenGL but was tied to Sun’s SPARC hardware, limiting adoption. SGI’s dominance in high-end graphics, fueled by OpenGL and its Geometry Engine, made it the preferred platform for Hollywood VFX (\Jurassic Park\**, 1993) and automotive design (Ford Taurus). However, SGI’s reliance on proprietary hardware left it vulnerable as NVIDIA’s GPUs and OpenGL’s portability shifted the market to PCs.

The 2009 release of OpenGL 3.2 introduced the Core Profile, removing deprecated features and optimizing for modern GPUs like NVIDIA’s GeForce series. This update enhanced complex surface rendering for ICEM Surf and CATIA on commodity hardware, further eroding the need for specialized workstations. OpenGL’s cross-platform support also enabled Maya to run on Windows and Linux, democratizing access to high-quality surfacing.

Successors and Modern APIs

By the 2010s, OpenGL faced challenges from Microsoft’s Direct3D, which dominated PC gaming with DirectX 9–11. Direct3D’s tight integration with Windows and support for NURBS tessellation in DirectX 11 (2010) made it a viable alternative for CAD and VFX. Apple’s Metal API (2014), designed for macOS and iOS, optimized GPU performance for surfacing in tools like Autodesk Flame, though its platform exclusivity limited adoption. The Khronos Group’s Vulkan (2016) addressed OpenGL’s inefficiencies, offering low-level GPU access for real-time surfacing. Vulkan’s efficiency powers Unreal Engine 6’s holographic NURBS, enabling AR/VR design for Meta’s Horizon Worlds.

WebGL (2011), based on OpenGL ES, brought surfacing to browsers, enabling cloud-based CAD platforms like Onshape. WebGPU (2023), a successor to WebGL, further enhanced browser-based rendering, supporting AI-driven surfacing for medical visualization. These modern APIs integrate with Neural NURBS and Adaptive Mesh Refinement (AMR), enhancing Hollywood VFX (\Tomb Raider II\**, 2025) and real-time surgical simulations. However, the shift to low-level APIs like Vulkan and DirectX 12 (2015) has increased developer complexity, sparking debates over accessibility versus performance.

Vendor Battles and Industry Impact

The API wars were as much about vendor strategy as technology. IBM’s graPHIGS faltered as its RS/6000 line lost ground to PCs, and by the late 1990s, IBM shifted focus to software like CATIA. HP’s Starbase faded as OpenGL became ubiquitous, though HP’s workstations adopted OpenGL for CAD. Sun’s XGL and SunGL (a partial OpenGL implementation) failed to gain traction, contributing to Sun’s decline before its 2010 acquisition by Oracle. As we mentioned before, SGI’s OpenGL success was bittersweet; while it standardized 3D graphics, NVIDIA’s CUDA and commodity GPUs rendered SGI’s hardware obsolete, leading to its 2006 bankruptcy.

Graphics APIs have been pivotal in surfacing’s evolution. PHIGS and graPHIGS enabled early CAD, OpenGL democratized high-quality rendering, and Vulkan and WebGPU support cutting-edge applications. These APIs have shaped industries by enabling precise, real-time visualization, from automotive Class A surfacing to medical imaging, while vendor rivalries drove innovation and disruption.]]> Michael Finocchiaro Kernel Wars https://demystifyingplm.com/chapter-8-the-evolution-of-surfacing-technologies-people-companies-and-the-creative-machines-behind-the-magic https://demystifyingplm.com/chapter-8-the-evolution-of-surfacing-technologies-people-companies-and-the-creative-machines-behind-the-magic Sat, 14 Jun 2025 18:43:55 GMT

Equally important in the evolution of the Kernel Wars, the battle for controlling surfaces - and ultimately the automotive body industry as well as the nascent Hollywood Special Effects industry, is the history of surfacing technology. It is not only a tale of mathematical innovation—it is one of brilliant individuals, storied companies, and groundbreaking applications that have shaped everything from cars and aircraft to Hollywood creatures. Behind each curve and smooth surface lies a lineage of ambition and sometimes failure that fueled the digital design revolution.

A Timeline about Surfacing and NURBS

The Pioneers of Curves: Bézier, de Casteljau, and Versprille

The Peugeot 204: it may not look like much, but it was the first design to use Bézier curves

Pierre Bézier, an engineer at Renault in the 1960s, developed what we now call Bézier curves and surfaces. They provided designers with a way to manipulate complex shapes intuitively, long before interactive 3D CAD existed. Paul de Casteljau at Citroën simultaneously explored similar curve systems using recursive algorithms—his work laid the mathematical foundation, while Bézier’s name became the standard.

In the 1970s, Dr. Ken Versprille, then a PhD student at Syracuse University, extended these ideas into NURBS (Non-Uniform Rational B-Splines). His insight: with rational weights and local control, NURBS could represent both conic sections and freeform shapes. This was the key that made them invaluable in both engineering and animation.

Volkswagen, Control Data, and the Birth of ICEM Surf

In 1983, this VM Mk2 Golf was the first car designed with what became ICEMsurf

Volkswagen, looking to design sleeker cars in the late 1970s, developed VWSurf in partnership with Control Data Systems. This was later commercialized as ICEMsurf, which quickly became the industry standard for “Class A” surfaces—those demanding high aesthetic and manufacturability standards.

ICEM Surf’s rise was shaped by its success on European luxury vehicles like the VW Golf and Ford Taurus, and later by brands like BMW and Porsche. The software focused on high-end surfacing quality and real-time reflection analysis, which was pivotal for luxury and sports car design. In 2007, Dassault Systèmes acquired ICEM Surf, integrating it into CATIA V5 as “CATIA ICEM Shape Design.”

The Canadian and Californian Contenders: Alias and CDRS

The current version of Autodesk Alias

In 1983, a Toronto-based team launched Alias/1, which brought real-time NURBS to SGI workstations. Alias soon became essential in industrial design and animation. Used to model everything from the Mazda RX-7 to the dinosaurs in Jurassic Park, Alias was eventually acquired by Autodesk in 2006 and remains a key product in automotive design.

Simultaneously, Evans & Sutherland developed CDRS (Conceptual Design and Rendering System), used extensively by Chrysler and other manufacturers. CDRS focused on ergonomic conceptual modeling and later inspired key elements of PTC’s early surfacing modules.

Stardent’s Dissolution and AVS’s Legacy

The n8n-like UX of AVS/Express

Meanwhile in Toronto, Stardent, born from a merger between Ardent and Stellar, created AVS for scientific surface rendering. AVS evolved into AVS/Express, used in molecular and geophysical visualization. It was notable for its integration of visual workflow for creating graphics images. It lives on at avs.com as an innovative data visualization toolkit, but competes with open-source platforms like VTK and ParaView.

Beyond the Obvious: Modern Surfacing Excellence

The Porsche Tacan: Top Gear!

Some of the finest examples of modern surfacing aren’t defined by gimmicks or media hype, but by their mastery of curvature continuity, light reflection, and manufacturability:

• Lucid Air: Designed with Alias and CATIA, its aerodynamic surfaces and subtle detailing reflect true Class A modeling.
• Rimac Nevera: This Croatian electric hypercar features intricate airflow channels, modeled with a blend of ICEM Surf and VR-based review tools.
• Porsche Taycan: Leveraging Dassault’s surfacing tools, it shows advanced continuity in curvature across fenders, doors, and spoilers.

From NURBS to Neural: Surfacing in the Age of AI

Holographic modeling from Neural Concept

The 2020s have brought a new generation of surfacing tools. Neural NURBS, powered by machine learning, suggest optimal control point layouts for a designer’s intent. Adaptive Mesh Refinement now tailors tessellation in real-time. Open standards like OpenPBR ensure that materials look consistent across rendering platforms, from Substance 3D to Unreal Engine 6.

Even holographic modeling is emerging, with companies like Neural Concept pioneering tools that blend topology optimization, AR/VR visualization, and surface continuity.

The frontier now lies not in manual control, but in intelligent delegation. The question has shifted from Can we model this? to What’s the smartest way to model this—collaboratively, precisely, and in real time?

Here is block math:

$$ \\int\_a^b f(x), dx = F(b) - F(a) $$

Boolean Operations: The Set Theory Crucible (ℝ³ → ℤ³)

Boolean operations on solids represent the first computational barrier that demanded hardware acceleration. The regularization of set operations in 3D space requires solving:

$$ S\1 \\otimes S\2 = \\text\{closure\}(\\text\{interior\}(S\1 \\star S\2)) $$

$$ \\Delta \\mathbf\{P\}i = \\mathbf\{P\}\{i+1\} - \\mathbf\{P\}\_i $$

Where $\\otimes$ represents regularized union/intersection/difference, and $\\star$ is the standard set operator. The conversion from continuous math to discrete implementations introduces topological challenges formalized by the Jordan-Brouwer separation theorem:

$$ \\partial S \\text\{ partitions \} \\mathbb\{R\}^3 \\text\{ into \} \\text\{int\}(S), \\text\{ext\}(S), \\text\{ and \} \\partial S $$

This theorem underpins all solid modeling kernels but requires combinatorial explosion management when implemented. For two meshes with $n$ triangles each, the intermediate intersection curve calculation has complexity:

$$ \\mathcal\{O\}(n^\{2/3\} \\log n + k) $$

where $k$ is the number of intersections (Shewchuk, 1999). This mathematical reality made early CAD systems computationally prohibitive without dedicated hardware.

Bézier & NURBS: The Parametric Revolution

Bézier Geometry (Bernstein Basis)

The parametric form of Bézier curves hides profound mathematical depth:

$$ \\mathbf\{B\}(t) = \\sum\\{i=0\}^n \\underbrace\{\\binom\{n\}\{i\} t^i (1-t)^\{n-i\}\}\\{\\text\{Bernstein polynomial\}\} \\mathbf\{P\}\_i $$

The derivative reveals its connection to differential geometry:

$$ \\Delta \\mathbf\{P\}i = \\mathbf\{P\}\{i+1\} - \\mathbf\{P\}\_i $$

$$ \\mathbf\{B\}'(t) = n \\sum\_\{i=0\}^\{n-1\} \\Delta \\mathbf\{P\}i \\cdot B\{i,n-1\}(t) $$

Where $\\Delta \\mathbf\{P\}i = \\mathbf\{P\}\{i+1\} - \\mathbf\{P\}\_i$. This structure enables parallel evaluation - a concept later exploited by GPU stream processors.

NURBS: Projective Geometry in Practice

NURBS introduce weights $w\_i$ and knot vectors $U$ through rational parameterization:

$$ \\mathbf\{C\}(u) = \\frac\{\\sum\\{i=0\}^n N\\{i,p\}(u) w\i \\mathbf\{P\}i\}\{\\sum\{i=0\}^n N\\{i,p\}(u) w\_i\} $$

The B-spline basis functions $N\_\{i,p\}$ follow recursive evaluation:

$$ N\\{i,p\}(u) = \\frac\{u - u\i\}\{u\\{i+p\} - u\i\} N\\{i,p-1\}(u) + \\frac\{u\\{i+p+1\} - u\}\{u\\{i+p+1\} - u\\{i+1\}\} N\_\{i+1,p-1\}(u) $$

This recursion depth leads to $\\mathcal\{O\}(p^2)$ complexity per evaluation point - a key driver for early GPU fixed-function hardware.

While parametric representations offer powerful mathematical tools for surface design, real-world engineering models often require operations on discrete mesh representations. This necessity leads us to examine the sophisticated mathematics of mesh manipulation and implicit modeling.

Mathematics of Mesh Healing, Repair, and Implicit Models

Mesh Healing and Repair: Radial Basis Functions and Parametric Mapping

Mesh healing represents a critical computational challenge in MCAD systems, addressing real-world model defects including holes, non-manifold edges, and self-intersections. The mathematical foundations of mesh repair bridge differential geometry, numerical analysis, and topology.

RBF Surface Interpolation

The Radial Basis Function approach provides a powerful mathematical framework for reconstructing surfaces from damaged meshes:

$$ s(\\mathbf\{x\}) = \\sum\\{i=1\}^N \\lambda\i , \\phi(|\\mathbf\{x\} - \\mathbf\{x\}\_i|) + p(\\mathbf\{x\}) $$

Where:

• $\\phi(r)$ represents the radial basis function (commonly Gaussian or thin-plate spline)
• $\\lambda\_i$ are weights determined by solving a linear system
• $p(\\mathbf\{x\})$ is a polynomial term ensuring affine invariance

$$ \\Delta u = 0 \\quad \\text\{on the surface\} $$

The solution to this PDE creates harmonic maps that preserve geometric features while repairing topological inconsistencies - a mathematical approach that parallels the regularization techniques in modern neural networks.

Parametric Remeshing

The mathematical elegance of surface parameterization transforms mesh repair into a 2D problem:

$$ f: \\mathcal\{M\} \\subset \\mathbb\{R\}^3 \\rightarrow \\Omega \\subset \\mathbb\{R\}^2 $$

This mapping minimizes distortion through energy functionals:

$$ E(f) = \\int\_\{\\mathcal\{M\}\} |\\nabla f|^2 dA $$

The resulting parameterization enables robust triangulation algorithms that would be combinatorially intractable in 3D space - demonstrating how dimension reduction (a concept central to modern AI) originated in graphics mathematics.

Implicit Models and Meshes

Implicit modeling represents a paradigm shift from explicit mesh representation, defining surfaces as level sets:

$$ F(x, y, z) = 0 $$

This representation creates a complete partition of 3D space:

• $F(x, y, z) < 0$ (interior)
• $F(x, y, z) = 0$ (surface)
• $F(x, y, z) > 0$ (exterior)

Boolean Operations on Implicit Models

The mathematical elegance of implicit representations transforms complex Boolean operations into simple algebraic expressions:

$$ F\\{A \\cup B\}(x, y, z) = \\min(F\A(x, y, z), F\_B(x, y, z)) $$

$$ F\\{A \\cap B\}(x, y, z) = \\max(F\A(x, y, z), F\_B(x, y, z)) $$

$$ F\\{A \\setminus B\}(x, y, z) = \\max(F\A(x, y, z), -F\_B(x, y, z)) $$

This R-function approach avoids the combinatorial explosion of mesh-based Boolean operations, providing mathematical robustness that directly influenced modern neural implicit representations.

Distance Fields and Level Sets

Signed distance fields (SDFs) represent a specialized implicit form:

$$ F(x, y, z) = \\pm \\min\_\{\\mathbf\{p\} \\in \\partial \\Omega\} |\\mathbf\{x\} - \\mathbf\{p\}| $$

The evolution of level set methods through the Hamilton-Jacobi equation:

$$ \\frac\{\\partial \\phi\}\{\\partial t\} + H(\\nabla \\phi) = 0 $$

This mathematical framework enables topology-changing operations that would be prohibitively complex with explicit meshes - a concept that later influenced neural network architectures for 3D shape generation.

Mesh Generation from Implicit Functions

The Marching Cubes algorithm bridges implicit and explicit representations through isosurface extraction:

$$ \{\\mathbf\{x\} \\in \\mathbb\{R\}^3 : F(\\mathbf\{x\}) = c\} $$

This algorithm samples the scalar field on a regular grid and constructs a piecewise linear approximation of the isosurface - a discretization process mathematically analogous to the quantization operations in modern neural networks.

Having explored the mathematics of both parametric and implicit representations, we now turn to the fundamental transformations that allow these geometric entities to be positioned, oriented, and projected in three-dimensional space.

The Matrix Revolution: Homogeneous Coordinates

The 4D projective space formulation enables efficient transformations:

$$

\\begin\{bmatrix\} a & b & c & t\_x \\ d & e & f & t\_y \\ g & h & i & t\_z \\ 0 & 0 & 0 & 1 \\end\{bmatrix\} \\begin\{bmatrix\} x \\ y \\ z \\ 1 \\end\{bmatrix\} $$

But the true power emerges in composition:

$$ \\mathbf\{M\}\{\\text\{total\}\} = \\mathbf\{M\}\{\\text\{proj\}\} \\cdot \\mathbf\{M\}\{\\text\{view\}\} \\cdot \\mathbf\{M\}\{\\text\{model\}\} $$

Matrix concatenation follows the Thompson group structure, requiring 4x4 matrix multiplication at 60+ FPS - a task impossible for 1990s CPUs but ideal for GPU parallelization.

Rendering Equations: Light as Integrals

The path from Phong shading to ray tracing rests on solving the rendering equation (Kajiya, 1986):

$$ L\o(\\mathbf\{x\}, \\omega\o) = L\e(\\mathbf\{x\}, \\omega\o) + \\int\\{\\Omega\} f\r(\\omega\i, \\omega\o) L\i(\\mathbf\{x\}, \\omega\i) (\\mathbf\{n\} \\cdot \\omega\i) d\\omega\i $$

Monte Carlo integration transforms this into:

$$ L\o \\approx \\frac\{1\}\{N\} \\sum\\{k=1\}^N \\frac\{f\r L\i (\\mathbf\{n\} \\cdot \\omega\\{i\k\})\}\{p(\\omega\\{i\k\})\} $$

With variance reduction requiring importance sampling:

$$ p(\\omega\i) \\propto f\r(\\omega\i, \\omega\o) (\\mathbf\{n\} \\cdot \\omega\_i) $$

This mathematical structure directly inspired importance sampling in variational autoencoders and modern denoising techniques.

GPU Architecture: Mathematics Made Silicon

The computational patterns forced GPU designers to create:

• SIMT Architecture: Single Instruction Multiple Thread
• Hierarchical Memory: Registers → Shared → L1/L2 → Global
• Tensor Cores: Mixed-precision matrix units

Operation	Graphics	AI
Matrix Multiply	View/projection transforms	Neural network layers
Reduction	Z-buffer depth test	Loss calculation
Filtering	Texture sampling	Attention mechanisms

The AI Symbiosis: From Polygons to Parameters

The mathematical throughline becomes clear:

Graphics Kernel	AI Operation
Vertex shader matrix transforms	Neural network layer $Wx + b$
Texture filtering	Convolutional neural networks
Marching cubes isosurfacing	Decision boundary visualization
Monte Carlo ray tracing	Bayesian neural networks
Photon mapping	Particle filter methods

The 2012 AlexNet breakthrough used 2.9 million CUDA cores (NVIDIA GTX 580) - hardware originally designed for graphics math.

Quantum Connections: Hilbert Space Meets Vertex Shaders

The mathematical tools developed for graphics find new life in quantum computing:

• Qubit State Visualization: Uses Marching Cubes algorithm
• Quantum Circuit Simulation: Leverages sparse matrix ops from FEM
• Quantum Machine Learning: Uses GPU-accelerated tensor networks

$$ \\rho = \\sum\i p\i |\\psi\i\\rangle \\langle\\psi\i| $$

Requires the same Hermitian inner product calculations as BRDF lobe sampling.

Conclusion: The Unbroken Mathematical Chain

From the Boolean algebra of CSG to the tensor cores in modern GPUs, the mathematical demands of computer graphics created:

• Hardware Architectures for massive parallelism
• Numerical Libraries for matrix/tensor operations
• Algorithmic Paradigms for approximate integration

• Transformer models ($\\mathcal\{O\}(n^2)$ attention matrices)
• Physics-informed neural networks (PDE discretization)
• Quantum computing simulations (Kronecker products)

Sources]]> Michael Finocchiaro Kernel Wars https://demystifyingplm.com/chapter-6-from-parametric-roots-to-direct-evolution-the-rise-of-hybrid-modeling-in-cad-kernels https://demystifyingplm.com/chapter-6-from-parametric-roots-to-direct-evolution-the-rise-of-hybrid-modeling-in-cad-kernels Thu, 12 Jun 2025 20:18:14 GMT

While Autodesk’s decision to fork ACIS and build ShapeManager underscored the strategic value of kernel independence, it also highlighted another emerging challenge: evolving those kernels to keep pace with user expectations. As we have seen, in the early 2000s, CAD users were increasingly demanding flexibility—not just in terms of vendor lock-in, but in how they could model. The rigid, feature-driven workflows of traditional parametric CAD were giving way to a desire for more intuitive, geometry-centric interaction.

The evolution of geometric modeling in CAD has long been framed as a rivalry between two approaches: parametric modeling and direct modeling. For decades, CAD systems were architected around one or the other, each championing different priorities. But over time, these distinctions began to blur as major modeling kernels integrated support for both paradigms. Today, hybrid modeling is the norm rather than the exception.

Parametric Modeling: Constraint-Driven Precision

Parametric modeling, popularized in the 1980s and 1990s by systems like Pro/ENGINEER and CATIA V5, is built around the idea of design intent. In this approach, geometry is defined not just by shapes but by a hierarchy of features, constraints, and dimensional parameters. These parameters drive geometry updates—change a number, and the model updates predictably.

Strengths:

• Highly structured
• Reproducible and editable
• Ideal for controlled design processes (e.g. regulatory compliance, manufacturing constraints)

• Can be rigid and slow to edit
• Complex history trees become fragile
• Not ideal for conceptual or iterative modeling

Direct Modeling: Geometry Without Baggage

Direct modeling, by contrast, emerged in tools like CoCreate and later SpaceClaim. It allows engineers to push, pull, and reshape geometry directly without being limited by parametric constraints or feature trees. The goal is speed and flexibility—particularly useful for conceptual design, simulation prep, or editing models from external sources.

Strengths:

• Fast and intuitive
• Great for legacy data and concept work
• Excellent for multi-CAD workflows

• Lacks design intent unless re-parameterized
• Less predictable for controlled design revisions

The Hybrid Breakthrough: Integrating Both Worlds

As users increasingly demanded the best of both worlds, CAD vendors and kernel developers began to integrate direct editing capabilities into parametric systems, and vice versa. This was not merely a UI change—it required deep changes at the kernel level to handle both representations and allow interoperability.

Milestone 1: CATIA V5 and CGM (1999)

Dassault Systèmes' CATIA V5, released in 1999 with the CGM kernel, was among the first major platforms to enable hybrid modeling. Though its UI still leaned parametric, the underlying kernel allowed for operations that bypassed strict history-based editing. CGM supported feature-based parametric modeling while also enabling operations like Boolean edits or surface reshaping without complete regeneration.

Milestone 2: Siemens' Synchronous Technology (2008)

The breakthrough for Parasolid came in 2008 when Siemens introduced Synchronous Technology with Solid Edge ST1, followed shortly by NX 7. This innovation combined the constraint solving and feature recognition of parametric systems with the direct geometry manipulation of direct modelers, tightly integrated at the Parasolid kernel level. It allowed users to apply direct edits while preserving (or re-deriving) design intent.

Milestone 3: PTC Wildfire 5.0 and Creo Flexible Modeling (2009–2011)

PTC took a different path. Starting with Wildfire 5.0 in 2009, it introduced the Flexible Modeling Extension (FMX), a set of tools for direct editing of parametric geometry. When PTC launched Creo in 2011, FMX was fully integrated into Creo Parametric, allowing users to push and pull geometry while retaining key constraints and relationships—implemented directly in the Granite kernel.

Milestone 4: Onshape and Cloud-Native Hybrid Modeling (2019)

In 2019, Onshape—a fully cloud-native CAD platform built on the Parasolid kernel—introduced its own flavor of hybrid modeling. While Onshape had supported parametric design from its inception, it added direct editing capabilities deeply integrated with its collaborative, version-controlled environment. Leveraging the flexibility of Parasolid and the scalability of the cloud, Onshape delivered hybrid modeling as a seamless, real-time experience for distributed teams.

Also in 2019, Siemens announced Convergent Modeling in their Parasolid kernel permitting both feature- and facet-based modeling at the core foundation of their modeler giving users unprecedented power over creating complex surfaces while maintaining geometric integrity.

Notable Absence: ACIS and Fragmented Support

Unlike Parasolid, CGM, or Granite, the ACIS kernel has seen more fragmented adoption of hybrid modeling. While it powers tools like BricsCAD and was once used in Inventor, few ACIS\-based systems offer native support for deeply integrated parametric+direct workflows at the kernel level. Instead, hybridization—when it exists—is often handled at the application layer.

Other Players and Proprietary Paths

While the major CAD vendors rely on well-known kernels like Parasolid, CGM, and Granite, a few niche tools follow different strategies. ZW3D and VariCAD use proprietary kernels, allowing them to tightly control modeling behavior at the cost of ecosystem integration. IronCAD, uniquely, uses a dual-kernel architecture, incorporating both ACIS and Parasolid. This provides users with access to both direct and parametric tools within a single environment—albeit with some added complexity.

Conclusion: The Hybrid Kernel Era

Today, nearly every major CAD system offers hybrid modeling, but how deeply this is supported depends on kernel capabilities. The shift from "parametric vs. direct" to "parametric + direct" has redefined modeling expectations and transformed how engineers interact with 3D geometry. Far from being a mere UI convenience, hybrid modeling reflects a fundamental shift in kernel architecture, data structures, and user philosophy.

Before we talk about some of the more technological aspects of the MCAD world, let's take a detour through the mathematical underpinnings of this world!

Not every story of a CAD adventure has a happy ending. Here are a few use cases that have some business lessons to teach us.

PTC's Mid-Market Misadventure: The Pro/JR Catastrophe

While Solid Edge and SolidWorks were successfully conquering the mid-market, the established high-end leader PTC was facing an uncomfortable reality: their customers were increasingly asking for more affordable alternatives to Pro/ENGINEER. PTC's response would become one of the industry's most cautionary tales.

In what can only be described as a catastrophic miscalculation, PTC launched Pro/JR in 1995—a stripped-down version of Pro/ENGINEER intended to compete with the emerging Windows-based solutions. The product was hampered by artificial limitations, poor performance, and a pricing strategy that satisfied neither high-end nor mid-market buyers.

Pro/JR's failure was so complete that it accelerated customers' migration to SolidWorks and other competitors. Rather than protecting PTC's market share, the initiative inadvertently validated the very products it was meant to compete against. The debacle reinforced PTC's eventual decision to focus exclusively on their high-end geometry engine.

In 2007, when PTC realized they needed a direct modeler for some scenarios to complement their parametric modeler, they acquired what was then CoCreate Solid Designer and rebranded it Creo Elements/Direct. This product, however, relies on a proprietary, ACIS-based kernel (see the timeline later in this article for details).

It wasn’t until 2019 that they purchased former PTCer and co-founder of SolidWorks, Jon Hirschtick’s Onshape (based on Parasolid kernel) for attacking the mid-market. I asked Steve Dertien, CTO of PTC, about whether Parasolid was still in use by Onshape:

Onshape as acquired is still based on Parasolid.  That's not easy to change, but we're also not exclusive.  We've already incorporated the Frustum kernel (acquired by PTC in 2018) for generative design as well as the Creo kernel for some other features. Similarly, Creo and all other CAD, do plug in other people's engines for features.  For example, we don't hide that we use Materialize (from Materialize NV in Belgium) in 3D Printing or ModuleWorks (from ModuleWorks GmbH in Germany)) for CAM simulation as well as Keyshot (from Luxion Inc in Costa Mesa, CA, USA) for rendering and Ansys for simulation.  Even when we added Ansys we still had to support the prior generations of simulations to maintain all the data feature compatibility. Every company decides where to build, buy and partner for technology in the stack where appropriate.

The Short, Somewhat Unhappy Life of CADDS5

CADDS5 was the final evolution of a CAD lineage dating back to CADDS1 in 1969, one of the earliest commercial drafting systems. Developed by Computervision, CADDS evolved through multiple generations — from 2D drafting to wireframe 3D (CADDS3) and eventually solid modeling (CADDS4X and CADDS5) in the 1980s. Unlike emerging kernels like Parasolid and ACIS, CADDS5 used a fully proprietary geometric modeling kernel, tightly integrated and never licensed or externalized. What made CADDS5 unique was its ability to support both direct modeling and parametric modeling, albeit via separate modules and executables — a powerful concept that prefigured later hybrid workflows.

After PTC acquired Computervision in 1998, it maintained CADDS5 for legacy industries like aerospace and shipbuilding, where long product lifecycles and regulatory lock-in made modernization difficult. But CADDS5 was eventually frozen at version 16.1 in 2013, with no future development. Its demise was due to several factors: a lack of modularity, no effort to license or replatform the kernel, and user resistance to its dated architecture and fractured modeling workflows. Meanwhile, the industry moved toward unified parametric-direct hybrid platforms like Solid Edge and, eventually, Creo.

Ironically, PTC’s later development of Creo did absorb some key lessons from CADDS5’s dual-mode modeling and large-assembly experience — but did so from a clean slate, not by reusing the CADDS kernel. The takeaway lesson is this: technological sophistication alone doesn’t ensure survival — adaptability, openness, and ecosystem strategy matter more than internal power. CADDS5 was ahead of its time in hybrid modeling but failed to evolve into an open platform others could build on. In the “kernel wars,” closed systems lost.

Forked at the Source: Autodesk’s Break from ACIS

Screenshot from Autodesk Inventor

When Autodesk set out to create Inventor—its answer to Pro/ENGINEER and SolidWorks—it knew it needed a robust 3D kernel. ACIS, then a rising player developed by Spatial Technology, was the obvious choice: proven, available, and already embedded in AutoCAD’s 3D extensions. But Autodesk made a bold move that would have long-term consequences: instead of fully committing to ACIS, they quietly forked the source code—creating their own derivative kernel called ShapeManager.

This gave Autodesk full control over the kernel’s evolution, independent of Spatial’s roadmap. But the story took a twist in late 2000, when Spatial was acquired by Dassault Systèmes, owner of SolidWorks—Autodesk’s rising nemesis. Suddenly, Autodesk found itself legally entangled with a competitor, accused of unpaid license fees on the forked code. Spatial sued. In 2003, Autodesk prevailed in court and retained royalty-free rights to its ShapeManager branch.

Meanwhile, Dassault cleaned up ACIS under Michael Payne’s leadership, fixing memory leaks and expanding functionality. But ACIS—once poised to challenge Parasolid—never regained the momentum it lost after both SolidWorks and Inventor abandoned it. While Spatial continues to license ACIS widely in mid-tier applications like BricsCAD and IronCAD, the kernel now sits behind the scenes, powering tools in markets where cost or compatibility matter more than cutting-edge modeling.

This episode isn’t just a legal footnote—it’s a striking example of kernel independence as strategic leverage. Autodesk’s decision to fork ACIS before Spatial’s acquisition gave it long-term autonomy, insulating Inventor’s roadmap from a now-rival platform. While rare, this approach has been mirrored by a few other vendors—notably CoCreate, whose SolidDesigner fork of ACIS still underpins Creo Elements/Direct. These cases serve as powerful reminders that owning your modeling core isn’t just a technical choice—it’s a business safeguard.

Today, ACIS continues to power some of the solutions of the CAD middle- and low-end markets through Spatial's OEM licensing program. Current ACIS\-based applications include Dassault Systèmes' DraftSight, BricsCAD, and IronCAD (in this case they have a dual-kernel with ACIS and Parasolid) and various CAM and CMM software vendors.

Another fascinating aspect is the parallel development of Intergraph Solid Edge and SolidWorks in the late 90s. Both companies saw the massive potential of the more powerful PCs and the Microsoft Windows NT operating system as ways of breaking into the mid-market space where UNIX machines were just simply too expensive and cumbersome. They would eventually settled on Parasolid for similar reasons, but their journeys from there reveal starkly different corporate cultures and decision-making processes.

Solid Edge: From ACIS to Parasolid

Early Integraph Solid Edge

Intergraph initially launched Solid Edge with the ACIS geometric kernel, developed by Spatial Technology. However, as the product matured, Intergraph's engineering team encountered scalability and adaptability challenges that threatened the platform's future growth. This is how Bill McClure, head of Solid Edge at the time, described the situation to me:

Initially, Solid Edge faced significant performance and reliability issues with some of the key functions of the ACIS kernel.  To address this, I initiated a clandestine "skunkworks" project, known only to myself and three other team members.  We even rented an apartment to work in secret, away from the office.  Our rapid evaluation of Parasolid quickly revealed its superior performance and reliability, solidifying our decision that we needed to switch Solid Edge to Parasolid. While the team continued to work on the Parasolid implementation offsite, I faced a challenging review meeting with the sales management team. The VP of Global Sales was highly concerned about the numerous customer and Application Engineer complaints regarding Solid Edge's modeling problems. The next day, Jim Meadlock (Intergraph CEO) demanded a solution. I revealed our secret project, explaining that our Parasolid implementation showed excellent test results.  I emphasized that switching to Parasolid was crucial for our survival in the Mechanical CAD market. Jim not only endorsed the move but also suggested we broaden our discussions with Unigraphics to explore a potential joint venture. This pivotal decision ultimately led to the acquisition of Intergraph's Mechanical Software Division by Unigraphics Solutions.

This transition occurred before Unigraphics acquired Solid Edge, setting the stage for the product's integration into what would become the Siemens PLM portfolio, a fact that might be surprising to those that assumed falsely that the move to Unigraphics was responsible.

SolidWorks: Granite Denied, Parasolid Adopted

Early screenshot of SolidWorks

SolidWorks' kernel story reveals the sometimes-personal nature of enterprise software decisions. There was initially a prototype built on ACIS, but due to similar issues that Solid Edge has seen in their experience, they tested Parasolid as well. SolidWorks also approached PTC's CEO Dick Harrison in 1995 with a request to license PTC's proprietary geometry engine—the same geometric engine powering Pro/ENGINEER. Harrison declined to commercialize it. Mike Payne told me the story this way,

We started SolidWorks using a trial copy of ACIS, but it was full of bugs. Can you imagine a graphics kernel at the heart of your code bleeding memory like a stuck pig? I reached out to Dick [Harrison, CEO of PTC at the time] and asked him if we could license the code for the geometry engine from Pro/ENGINEER. He gave me the side-eye and said, 'But we don't do that, sell the engine, I mean.' I countered, 'That doesn't mean you can't start doing it now, though.' He just stood there and after a beat said, 'But we don't have a model for selling it.' So, that wasn't going to happen. As it turns out, I had already created libraries in parallel for plugging either ACIS or Parasolid into SolidWorks and found that Parasolid fixed most of our bugs and was much faster, so the decision to switch to Parasolid was easy. As time went on, Pro/ENGINEER would refuse to benchmark against us, so I guess that tells you how it worked out for SolidWorks at the end!

Harrison's refusal would prove consequential. PTC never built a model for commercializing their graphics engine because they didn't want to become an OEM for software. They preferred to focus on their core products and offer APIs for partners and customers to build on top of them (see the PTC Granite chapter above). Faced with this rejection by Dick to license the PTC graphics engine, SolidWorks signed a contract with Chuck Gridstaff at Unigraphics and adopted Parasolid, joining what would become a growing ecosystem of Parasolid-powered applications.

Author's Note: When I was working on this article and gathering these testimonials, it turns out that although Mike and Bill knew each other, they had no idea that each had struggled with simular ACIS problems (bugs and performance issues) and reached the same conclusion (they went to talk to Tony Affuso and settled on Parasolid). Both were surprised and amused when we talked about it. It is a small, weird world, the CAD/PLM world, for sure!

2025 Update: The Pattern Continues

In the opening months of 2025, ANSYS migrated SpaceClaim—originally developed by Mike Payne in 2005 and acquired by ANSYS in 2014—to theParasolid kernel, following in the footsteps of SolidWorks and Solid Edge in moving away from ACIS for improved robustness and interoperability. The product has since been rebranded as ANSYS Discovery.

The Great Divergence: Sales Strategy as Destiny

While both products shared similar technical foundations and target markets and released within a few months of each other, their sales strategies created vastly different trajectories. This divergence would ultimately determine which company would capture the larger share of the exploding mid-market CAD opportunity.

SolidWorks made a bet that would define its success: a channel-centric sales model. Rather than building a large direct sales force, the company partnered with regional resellers who could provide local support and relationships. This strategy proved remarkably effective, enabling rapid geographic expansion and customer acquisition at a fraction of the cost of direct sales.

The results were spectacular. SolidWorks' growth rate outpaced Solid Edge, establishing market momentum that would prove difficult to reverse. By 1997, just a few years after launch, SolidWorks had attracted the attention of Dassault Systèmes, which acquired the company in a deal that would transform both organizations.

In contrast, Solid Edge used a traditional direct sales model and a smaller channel model and never achieved the explosive growth that SolidWorks experienced. Its acquisition by Unigraphics (later acquired by Siemens) positioned the product within a comprehensive PLM portfolio. While it never achieved SolidWorks' market dominance, Solid Edge found its niche as part of Siemens' broader industrial software strategy, particularly in manufacturing and engineering workflows.

Let's now take a look at the three primary vendors that own graphics kernels and compare how they either kept them in-house or commercialized then.

PTC Revolutionizes MCAD with Parametric Modeling

PTC Creo Screenshot

Parametric Technology Corporation (PTC) developed its proprietary geometry engine headed by Leonid Raiz (later, one of the creators of Revit) to power its Pro/ENGINEER (now Creo) software, aiming for a robust, in-house solution to support parametric and history-based modeling. It was the world's first fully parametric geometry engine and was an instant success. They ran on UNIX workstations and later Windows PCs, but offered an unprecedented level of flexibility in modeling that changed the ways many products were designed outside the traditional aerospace & defense and automotive niches where PLM had been confined prior to this.

PTC Fact Sheet for commercialized Granite kernel

In 2014, PTC started to market APIs for their proprietary geometry engine giving it a name for the first time, Granite. They had hoped to create an eco-system of Creo apps, but they found that it generated little interest on a market more geared towards interoperability. PTC, continues to develop Granite in its Creo products and still allows customers to leverage their APIs to build Creo apps (more on this later in the article).

Siemens Decides to Commercialize Parasolid

Unigraphics before the transformation to NX

By stark contrast, EDS Unigraphics (and later EDS PLM, UGS, Siemens PLM, and Siemens Digital Industries Software) in its various forms and corporate changes) kept to a consisently open framework. Tony Affuso, CEO of this group for over 30 years, told me:

We had a strong belief in our culture that the rich data that customers created with CAD/CAM/CAE (C3) products needed to be shared among many users across their company. If data sharing was successful, it would be extremely valuable to all customers in our industry and would enable the Digital Transformation of their products and their manufacturing processes.  You may recall that one of the largest obstacles to data sharing was that C3 vendors all had different proprietary data formats and it was very difficult (not to same occasionally impossible) to convert them into a usable format while maintaining data integrity and modeling history.  To help facilitate data sharing between C3 software applications in the 90’s, we created the Toolkit Group whose mission was to build a “the level playing field” for all of the licensees of our Parasolid kernel (and later, the other key kernels from D-Cubed (parametric constraints) and Vistagy (composites modeling)). The Toolkit Group was run as a separate business unit (now called Siemens PLM Components) that worked with all of our competitors using our kernels to ensure equal treatment in kernel technology upgrades and licensing schemes. Our belief was that the kernels were more of a commodity and that the competitive differentiation for software app developers was really the application on top of the kernels. As time has gone by, the teams & management involved with running “the level playing field” at UGS and later Siemens have licensed these toolkits to well over 200 software vendors and have achieved remarkable results in the enabling the rich data sharing for C3 customers across the globe.

This was really a turning point in the industry and we will see later than there are still many competitors to Siemens CAD products using Parasolid. Internally, the flagship CAD of Siemens, formerly called Unigraphics, now called NX, uses Parasolid.

Shapr3D on iPad

In 2015, CAD expert István Csanády realized the power of the newly introduced Apple iPad and decided to create the world's first CAD app native to iOS, baptizing it Shapr3D for his eponymous company. As to why he pivoted from the open source Open Cascade kernel from Capgemini Engineering (see chapter below) to the Parasolid kernel, István told me this:

It’s simply the most robust and fastest kernel on the market, also basically the industry standard, as NX, SOLIDWORKS are based on it, covering a very large chunk of the market. And it is the only industrial grade kernel that’s available on Windows, Mac, iOS and visionOS.

István mentions visionOS because they also were the first app demoed on the Vision Pro during the official Apple announcement at WWDC 2023. Their focus is industrial design and they are based in Budapest, Hungary.

The Histories of CGM and ACIS via CATIA and Spatial

CATIA V5

In contrast, Dassault Systèmes developed the CATIA Geometric Modeler (CGM) specifically for its CATIA software, with CGM becoming the core kernel starting with CATIA V5 in 1999 and continuing through to the 3DEXPERIENCE platform; earlier versions (CATIA V1 through V4) relied on different surface modeling technologies, with V4 using a proprietary kernel whereas CGM was built explicitly for CATIA V5.

I asked Alain Dugousset, CATIA Top Gun and enthusiast to explain this to me:

“On CATIA V3, our kernel was a solid modeler (SolidM) with some Boolean operations between them. With V4, rather than just facets (read “triangles”) the surfaces became mathematical surfaces with a first pass at Exact surfaces (read "NURBS support"), thus it was called SolidE. There were some initial experiments in parametric modeling because of the pressure from PTC’s explosive growth of Pro/ENGINEER. It was decided that a new architecture was necessary for the next generation (CNEXT) and so they created the CATIA Graphic Modeler for CATIA V5 which was the world’s first graphics engine that incorporated direct modeling, exact surface modeling, and parametric modeling in the same kernel. It has continued to improve for very small assemblies (watch mechanisms) and very large assembles (buildings, bridges, and cities) as it evolved to CATIA V6 and the latest incarnation, CATIA 3DEXPERIENCE. It is a dominant player in mechanical industries such as aerospace, automotive, and industrial equipment. It’s my favorite CAD package, can you tell?”

CATIA V5 marked a complete break from its predecessor—not just in interface or architecture, but in philosophy. Where V4 had been tailored largely to Boeing’s stringent surface modeling needs, V5 was a true blank-page initiative: reimagined in C++ and built to be more accessible, with usability lessons drawn from SolidWorks as well as the previous experiments in SolidM and SolidE, and a deliberate effort to avoid the perceived complexity of Pro/ENGINEER.

As Didier Bourcier, the lead developer of CATIA V5 explained,

With CATIA V5, we didn’t just update the old system—we started from scratch. The move from FORTRAN we used in V4 to C++, the replacement of the legacy geometric modeler, and a complete rethink of the system architecture were all necessary to meet the demands of modern engineering and embrace the rise of Windows workstations. Even the constraint solver (initially D-Cubed) was replaced with a custom-built engine we fully owned.

The real driving force behind V5’s evolution was Toyota, whose deep expertise in surface modeling, user workflows, and design-change stability pushed Dassault to rethink everything—from topological tracking to modification robustness as well as ease of use. The early CGM kernel, initially prone to cascading failures from small edge modifications, matured into a topology-aware modeler under the guidance of both internal champions like Didier Bourcier (quoted just above) and relentless customer pressure. As Jacques Léveillé-Nizerolles, former CEO of CATIA, put it:

The CGM kernel wasn’t just engineered — it was shaped by the hands of our clients. Toyota, Boeing, Honda… they didn’t just push for features; they pushed us to rethink robustness, surface control, and the very complex relationship between user and geometry. Without them, CATIA wouldn’t be what it is today.”

Thousands of evolution requests from Toyota alone shaped V5 and V6 over multiple versions. Dassault’s future, it became clear, would depend not just on innovation, but on deep, sustained collaboration with the world’s most exacting manufacturers.

CATIA V6

In 2008, DS make the revolutionary decision to break the "file-based" paradigm and store all CATIA V6 data in the ENOVIA V6 database instead. Needless to say, users were a bit surprised to lose the File-Open menu item. However, the idea of storing the CAD data in a database was not new. There had been several attempts to do this using Oracle BLOBS, but they were typically performance catastrophies. Notably, ENOVIA V5 managed CAD data in the database with a "blackbox" option to use a filesystem which became popular. Nonetheless, CATIA V5 is most commonly used with CATPart files whereas CATIA V6 and CATIA 3DEXPERIENCE no longer offer a "file-based" option.

Stäubli Robot Studio

By 2011, Dassault’s subsidiary, Spatial Technologies (acquired by DS in 2000), began selling CGM as a standalone component to Mitsui Zosen Systems Research Inc. (MSR). As recently as 2022, it was adopted by robotic firm Stäubli for its Robotics Suite, leveraging CGM’s compatibility with CATIA V5 and CATIA 3DEXPERIENCE.

BricsCAD screenshot

After the aquisition of Spatial, Dassault cleaned up ACIS fixing memory leaks and expanding functionality. But ACIS—once poised to challenge Parasolid—never regained the momentum it lost after both SolidWorks and Autodesk abandoned it (those stories coming up soon!). While Spatial continues to license ACIS widely in mid-tier applications like Dassault Systèmes' DraftSight, BricsCAD and IronCAD (albeit in this case with a Parasolid dual-kernel) as well as a handful of various CAM and CMM software vendors, the kernel now sits behind the scenes, powering tools in markets where cost or compatibility matter more than cutting-edge modeling.

Siemens PLM Components versus Spatial Face-off

Now that we have seen the history of the graphics kernels commercialized by both Spatial (DS) and Siemens PLM Components, here is a handy comparison table of their offerings off of there respective websites.

Faceoff between Siemens PLM Components and Spatial

Notes:

\ The NX Open API is not sold by Siemens PLM Components, but is a development kit similar to what we saw for Granite so customers can build apps on top of NX.*

\\ Vistagy Fibersim is sold by the Specialized Engineering Solutions Group and not by Siemens PLM Components

Interestingly enough, other than the final two categories (for which Dassault has solutions in NETVIBES/ENOVIA and DELMIA respectively, just not externally licensed), the two companies stack up rather well. I find it surprising that 3DXML which DS has been promoting as a 3DEXPERIENCE exchange format doesn't show up here. In terms of overall market penetration, you'll have to read on - no spoilers!

Besides all of these proprietary kernels we have discussed, there is one open source project out there with a fascinating story: that's up next!

The Open Source kernel, Open Cascade's Fascinating History

Origins and Development

Euclid CAS.CADE

The CAD package Euclid was initially developed in the early 1970s by Jean Marc Brun and Michel Théron at the Laboratoire d’informatique pour la mécanique et les sciences de l’ingénieur (LIMSI) in France, focusing on modeling fluid flow. In 1979, they founded Datavision to commercialize their work, which was subsequently acquired by Matra, forming Matra Datavision in 1980. 

Throughout the 1980s and 1990s, Matra Datavision developed the Euclid-IS solid modeling 3D CAD software, notable for its hybrid modeling approach combining boundary representation (B-rep) and constructive solid geometry (CSG) techniques. 

Evolution and Open Sourcing

The original EUCLID Quantum CD for...Silicon Graphics!

In 1997, Matra Datavision introduced EUCLID QUANTUM, a new generation of their CAD system built on the CAS.CADE (Computer Aided Software for Computer Aided Design and Engineering) platform. 

By 1999, Matra Datavision transitioned CAS.CADE to open source, releasing it as Open CASCADE, which later became known as Open CASCADE Technology. 

Acquisition and Legacy

History of MDTV and Open CASCADE

In 1998, Dassault Systèmes acquired Matra Datavision, but stopped developing EUCLID, since it was redundant with the shortly-to-be-released CATIA V5, although EUCLID Styler and EUCLID Machinist survived in the CATIA V5 universe for few years until DS had absorbed the technology they could salvage from them into CATIA V5 and DELMIA V5.

Today, Open CASCADE Technology continues to be a foundational platform for various low-end CAD applications, maintained by Open Cascade Technologies (OCCT), a subsidiary of Capgemini Engineering acquired in 2014 at the end of a long series of acquisitions.

You'll see Open Cascade pop up again in this story a little later.

Before diving into the individual stories of CAD pioneers, it’s worth understanding the deeper technological foundations that shaped the evolution of modern CAD systems. The geometric modeling capabilities behind today’s software can be traced back to foundational research in solid modeling that began in the 1960s, particularly at the University of Cambridge’s Computer Laboratory. Under the direction of Professor Maurice Wilkes, Charles Lang established a CAD research group in 1965 that became a crucible of innovation in computational geometry and computer graphics.

Early breakthroughs emerged with the BUILD boundary representation modeler, developed by Ian Braid starting in 1969 under Lang’s supervision. BUILD tackled challenges in solid modeling—like exact surface-surface intersections—that others avoided through faceting. Alan Grayer joined in 1971, focusing on algorithms for machining prismatic parts modeled in BUILD, leading to one of the earliest integrations of CAD with CAM.

This pioneering work laid the groundwork for ROMULUS, a commercial solid modeling kernel developed at Shape Data Ltd., a company founded in 1974 by Braid, Grayer, Lang, and Peter Veenman. ROMULUS was released commercially in 1978 and became the kernel behind systems like HP’s ME30. In 1985, Shape Data began development of Parasolid as a more advanced successor to ROMULUS, with an improved architecture.

Later that year, Ian Braid, Alan Grayer, and Charles Lang left Shape Data to co-found Three-Space Ltd, collaborating with Spatial Technology Inc., a company founded by Dick Sowar in Colorado. Together, they developed ACIS, a completely new kernel released in 1989, known for its support for both manifold and non-manifold modeling, wires, sheets, and precision modeling techniques.  There is a popular story that ACIS' name was derived from its founders, "Alan, Charles, and Ian's System" and there is another that claims they credited the obscure Greek mythology around Acis in Ovid's Metamorphoses). I guess you can choose which one you prefer. Either way, the often-simplified notion that Parasolid and ACIS were developed independently misses the continuity: both kernels trace directly back to the Cambridge team that pioneered boundary representation modeling, and both were led or heavily influenced by the same core individuals.

While Parasolid and ACIS implement similar mathematical principles—such as B-rep, constructive solid geometry (CSG), and separation of geometry from topology—they are distinct codebases. Parasolid was originally written in FORTRAN and C before transitioning to C++, whereas ACIS was developed in C from the outset with object-oriented extensions. Cambridge’s UK legacy in geometric modeling, much like MIT’s in the U.S. PLM scene (see this article) in Cambridge, MA, continues to echo in nearly every major CAD system in use today.

What is a Graphics Kernel?

A graphics kernel is the unsung hero of CAD systems, managing the rendering and manipulation of graphical elements. It is responsible for handling the fundamental operations required to display and modify 3D models, ensuring precision and efficiency in design processes. The architecture of a graphics kernel is typically layered, with each layer contributing specific functionalities.

The Layered Architecture of Graphics Kernels

1. Geometric Modeling Kernel (Core Engine)

The geometric modeling kernel is the core component of any 3D CAD system. It performs solid and surface modeling using boundary representation (B-rep) geometry and supports operations like Booleans (union, subtract, intersect).

B-rep, or boundary representation, is a method for defining 3D geometry by its surfaces, edges, and vertices—allowing for precise control over complex shapes and topology. It is the foundation of most modern solid modeling systems.

These kernels also support exact surface representations such as NURBS (Non-Uniform Rational B-Splines), which are a mathematical model for smooth curves and surfaces. NURBS are critical in high-precision design, especially in automotive and aerospace, due to their ability to accurately describe freeform geometry.

Leading kernels include:

• Parasolid (used in Siemens NX and Solid Edge, DS SolidWorks, PTC Onshape)
• ACIS (used in Autodesk AutoCAD, BricsCAD)
• CGM (used in Dassault Systèmes CATIA V5 and 3DEXPERIENCE)
• Granite (used in PTC Creo)
• ShapeManager (a fork of ACIS used in Autodesk Inventor and Fusion 360)
• SolidDesigner kernel (a fork of ACIS used in PTC Creo Elements/Direct, formerly CoCreate)

2a. Part Modeling: Features, Constraints & Parametric Logic

This sits directly atop the geometric kernel to define and control individual part shapes.

• Adds parametric feature history (extrudes, holes, fillets, etc.) for design replay/editing.
• Defines sketches with geometric and dimensional constraints (e.g., parallel, equal, fixed).
• Enables direct modeling and push-pull interaction where parametrics aren't used.
• Maintains design intent via dimensions and expressions (e.g., holediameter = 2 * pinradius).
• Uses constraint solvers (e.g., Siemens D-Cubed 2D/3D DCM and Spatial's CDM) for solving geometry relationships.
• Provides topological tracking to maintain stability across edits.

2b. Assembly Modeling: Structure, Mates & Product Hierarchy

This one operates at the multi-part product level to manage relationships, structure, and motion.

• Supports mating conditions (coincidence, tangency, angle, distance) between parts.
• Enables kinematics and motion simulation of assemblies with moving components.
• Tracks instance relationships, part reuse, and subassembly structure.
• Builds the product BOM hierarchy (Bill of Materials).
• Supports lightweight geometry and interference detection for large assemblies.
• Handles spatial organization and positioning of parts in 3D space.

This separation reflects how CAD software is typically modularized internally:

• The part modeling engine is usually focused on feature trees and constraint solving.
• The assembly engine is often a distinct module handling spatial logic and performance at scale.

3. Visualization & Tessellation Layer

• Converts precise geometry into displayable triangular meshes for real-time 3D views.
• Interfaces with graphics engines (e.g., OpenGL, HOOPS) for shading, zoom, and rendering.
• Ensures fast viewport performance without compromising underlying accuracy.

4. The Application Layer: Where Innovation Meets the Engineer

If the geometry kernel is the beating mathematical heart of CAD, then the application layer is its visible face—the part users actually see, touch, and use to bring their ideas to life. This is where the abstract becomes tangible, where parametric models, direct editing, and digital threads are made accessible through intuitive interfaces and powerful workflows.

It's here you'll find your favorite MCAD tools and familiar user interfaces. Every time you sketch a profile, extrude a solid, or fine-tune a feature, you're interacting with this layer—sending instructions down to the kernel, which quietly handles the mathematical heavy lifting. The application layer is also home to advanced modules for CAM (generating toolpaths for CNC machining), automated assemblies, and cutting-edge generative design workflows. When you use generative design, AI-driven algorithms repeatedly query the kernel, exploring thousands of possible solutions in minutes—something unthinkable in the days of manual drafting.

But What About Meshing?

To simulate, test, or optimize a design, engineers turn to FEM/FEA (Finite-Element Meshing/Analysis) tools. Meshing is the process of breaking complex 3D models into smaller, solvable elements—a crucial step for simulations, from crash tests to thermal analysis.

Here's why this matters:

• Meshing tools often straddle the application and kernel layers.
• For high-fidelity results, they must tessellate (slice up) the exact geometry produced by the kernel.
• This means robust integration with kernel APIs is essential for accuracy and reliability.

Real-World Applications

Each kernel serves distinct strengths in real-world MCAD workflows.

• Parasolid, known for its robust Boolean operations and stability, excels in complex assemblies and history-based modeling.
• ACIS, with its flexible licensing, is favored in mid-tier CAD and direct modelers.
• CGM, tightly integrated into Dassault Systèmes' platform, supports high-precision surfacing and multi-discipline integration, ideal for aerospace and automotive engineering and design.
• Granite, developed by PTC, is optimized for parametric associativity and interoperability.

🥁 Here is your Official #FinoQuickTake Interviews and sessions summaries and assudries from the knockout success inaugural #SharePLMSummit in the superb Bodegas Fundador in (very) sunny Jerez, Spain. It is hard to do a final post, but here is a helpful index listed chronologically according to the agenda (although the interviews are not necessarily in the same order.

My apologies to Jakob because we forgot to hit record for his interview and to Patrick whose talk were somehow missing from the Otter.ai transcript completely (42 min just gone. Gotta check w/tech support 'cos, hey, I'm paying for it!)

So, without further ado:

Don’t miss my retrospective podcast in the #FinoPresents #FutureOfPLM for this inaugural event featuring conference speakers Maria Morris, Oleg Shilovitsky, Jos Voskuil, and Rob Ferrone and massive success - sign up here: https://lnkd.in/ePdRzQNd

Day 1

Breakfast Video Post: https://www.linkedin.com/posts/mfinocchiaro\puttingpeopleinplm-finopresents-futureofplm-activity-7333021730778578944-eOgQ

Welcome from Beatriz González Pedraza, CEO of Share PLM : https://www.linkedin.com/posts/mfinocchiaro\shareplm-puttingpeopleinplm-puttingpeopleinplm-activity-7333030008665366528-PkGb

Andrea Järvrén of Tetra Pack: Design Thinking

Summary of talk: https://www.linkedin.com/posts/mfinocchiaro\designsprints-puttingpeopleinplm-finopresents-activity-7333038135519449089-TMFy

Interview: https://www.linkedin.com/posts/mfinocchiaro\shareplmsummit-shareplmsummit-finoquicktake-activity-7333116098231328771-D5gJ

Ramón Lorca of Siemens Energy: Navigating Change

Summary of talk: https://www.linkedin.com/posts/mfinocchiaro\shareplmsummit-puttingpeopleinplm-puttingpeopleinplm-activity-7333084431697539072-CPdp

Interview: https://www.linkedin.com/posts/mfinocchiaro\finoquicktake-shareplmsummit-puttingpeopleinplm-activity-7333365844028149760-TvKj

Martin Eigner Interview: https://www.linkedin.com/posts/mfinocchiaro\shareplmsummit-puttingpeopleinplm-finopresents-activity-7333420025338359809-qFAK

Thelma Bonello of Methode Electronics: Designed by Humans, Outpaced by Machines

Summary of talk: https://www.linkedin.com/posts/mfinocchiaro\shareplmsummit-puttingpeopleinplm-puttingpeopleinplm-activity-7333102281036349440-SGfO

Interview: https://www.linkedin.com/posts/mfinocchiaro\shareplmsummit-puttingpeopleinplm-puttingpeopleinplm-activity-7333367245131145216-Hwod

Oleg Shilovitsky of OpenBOM: PLM's Missing Link

Summary of talk: https://www.linkedin.com/posts/mfinocchiaro\shareplmsummit-puttingpeopleinplm-datadriveninnovation-activity-7333572747865870341-XvfK

Interview: https://www.linkedin.com/posts/mfinocchiaro\finoquicktake-shareplmsummit-puttingpeopleinplm-activity-7333457378270461952-1mC4

Johan Mikkelä of FLSmidth: Unlocking Success

Summary of talk https://www.linkedin.com/posts/mfinocchiaro\shareplmsummit-puttingpeopleinplm-projectmanagement-activity-7333126390977863684-JqRk

Interview: https://www.linkedin.com/posts/mfinocchiaro\finoquicktake-shareplmsummit-puttingpeopleinplm-activity-7333411200346570753-uEFW

Interview of Rush Bittner of XPLM: https://www.linkedin.com/posts/mfinocchiaro\finoquicktake-shareplmsummit-puttingpeopleinplm-activity-7333366220064272385-W6Ng

Antonio Casaschi of ASSA ABLOY:

Interview: https://www.linkedin.com/posts/mfinocchiaro\finoquicktake-shareplmsummit-puttingpeopleinplm-activity-7333449530778017793-w6H9

Interview with Patrick Willemsen and Matthias Föhrer of Aras: https://www.linkedin.com/posts/mfinocchiaro\shareplmsummit-finoquicktake-puttingpeopleinplm-activity-7333364900062224384-ZpVb

Linda Kangastie of Valmet: Navigating Change

Summary of talk: https://www.linkedin.com/posts/mfinocchiaro\shareplmsummit-puttingpeopleinplm-digitaltransformation-activity-7333363754652372992-4PWQ

Interview: https://www.linkedin.com/feed/update/urn:li:activity:7333367094345977856/

Panel Discussion between Helena, Rob, Johan, Andrea, and Jos: https://www.linkedin.com/posts/mfinocchiaro\shareplmsummit-shareplmsummit-puttingpeopleinplm-activity-7333583796769865729-ua0O

Day 2

Breakfast Picture Post: https://www.linkedin.com/posts/mfinocchiaro\shareplmsummit-puttingpeopleinplm-puttingpeopleinplm-activity-7333376272783327232-u0IK

Helena Gutierrez of The Nest/Share PLM: The Future is Human

Summary of talk: https://www.linkedin.com/posts/mfinocchiaro\shareplmsummit-finoquicktake-shareplmsummit-activity-7333400119205117952-Y6S8

Interview: https://www.linkedin.com/posts/mfinocchiaro\finoquicktake-shareplmsummit-shareplmsummit-activity-7333120711768657920-Tho6

Jakob Åsell of Modular Management: Modularization

Summary of talk: https://www.linkedin.com/posts/mfinocchiaro\shareplmsummit-shareplmsummit-puttingpeopleinplm-activity-7333576925132554240-Qq8\

Philipp Ludwigt of FLSmidth: Race against time

Summary of talk: https://www.linkedin.com/feed/update/urn:li:activity:7333580688169873410/

Interview: https://www.linkedin.com/posts/mfinocchiaro\finoquicktake-shareplmsummit-puttingpeopleinplm-activity-7333557219281526785-M32j

Rob Ferrone: Death on the Shop Floor, A PLM Murder Mystery

Summary of talk: https://www.linkedin.com/posts/mfinocchiaro\the-world-premiere-of-the-great-plm-activity-7333522081856335872-eTSI?u

Interview: https://www.linkedin.com/posts/mfinocchiaro\finoquicktake-plmplumber-shareplmsummit-activity-7333441269362278400-xLNg

Jos Voskuil of TacIT: Humans Cannot Transform - Help Them!

Summary of talk: https://www.linkedin.com/posts/mfinocchiaro\shareplmsummit-puttingpeopleinplm-digitaltransformation-activity-7333122250331631617-BiMb

Interview: https://www.linkedin.com/posts/mfinocchiaro\finoquicktake-shareplmsummit-puttingpeopleinplm-activity-7333617227268554752-bIHg

Wedding, I mean lunch photo post: https://www.linkedin.com/posts/mfinocchiaro\shareplmsummit-puttingpeopleinplm-finopresents-activity-7333456422636056577-kLEg

Panel Discussion with Beatriz, Oleg, Martin, Andrea and Antonio

https://www.linkedin.com/posts/mfinocchiaro\shareplmsummit-puttingpeopleinplm-digitaltransformation-activity-7333574697755840514-P5Qb

Technical Workshop about AI led by Rob Ferrone: https://www.linkedin.com/posts/mfinocchiaro\shareplmsummit-shareplmsummit-puttingpeopleinplm-activity-7333584934009270272-XXL8

Maria Morris QuickTake: https://www.linkedin.com/posts/mfinocchiaro\finofasttake-shareplmsummit-puttingpeopleinplm-activity-7333546169148596224-qWGd

Rob Ferrone - Part 2: https://www.linkedin.com/feed/update/urn:li:activity:7333528792335843329/

Closing Flamenco song: https://www.linkedin.com/posts/mfinocchiaro\shareplmsummit-puttingpeopleinplm-activity-7333552111168708610-UbU\

Closing Photo with SharePLM: https://www.linkedin.com/feed/update/urn:li:activity:7333539552449515521/