OMRON and Dassault Systèmes are creating a link between 3D design and simulation in the virtual world, and robots, sensors and production lines in the physical world.

The collaboration combines Dassault Systèmes’ 3D UNIV+RSES with OMRON’s Sysmac industrial automation platform, enabling manufacturers to design, simulate, validate and deploy production systems within a continuous virtual environment. At the core of the partnership is the virtual twin of production systems.

"Manufacturing is entering a new era. With OMRON, we are building living production systems, AI-driven, self-improving, and software-defined, where the virtual and physical worlds are fused into one continuous loop of learning. Our industry world models transform complexity into intelligence, making factories not just automated, but autonomous. This is how we reinvent industrial systems, from reactive to predictive, from rigid to adaptive and define the next frontier of manufacturing," says Pascal Daloz, CEO, Dassault Systèmes.

"Our partnership with Dassault Systèmes strengthens our ability to integrate the OT and IT worlds and provide customers with a holistic solution from simulated to fully implemented, intelligent production," said Motohiro Yamanishi, company president of the Industrial Automation Company (IAB), OMRON Corp.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

This partnership builds on Siemens' supply chain intelligence vision, where Siemens' Supplyframe brings deep design-to-source intelligence across electronic components and Xometry extends that intelligence into standard and custom mechanical parts through its digital marketplace business and Thomas, Xometry's North American industrial sourcing network.

In addition to the integration within Siemens' Designcenter software, the partnership includes integration of Thomas, Xometry's North American industrial sourcing network, with Siemens' Supplyframe to bring design-to-source intelligence for electronic and mechanical components to source the bill of materials for Siemens' customers.

The partnership is accompanied by a minority investment into Xometry of approximately $50 million¹. By integrating design, pricing, sourcing insights and production insights across the digital thread, Siemens and Xometry are creating capabilities that neither company could deliver independently.

"Industrial competitiveness is defined by how fast and how confidently companies can turn digital ideas into physical reality," says Tony Hemmelgarn, president and CEO, Siemens Digital Industries Software. "By infusing Siemens' comprehensive digital twin expertise and industrial AI innovation with large-scale, AI-driven manufacturing intelligence, we're breaking down the boundary between design and production for our customers. Our partnership with Xometry enables us to leverage AI to deliver the intelligence captured from millions of manufactured custom parts directly into the design process, empowering designers to work smarter, faster, and with greater impact."

"Xometry and Siemens share a common opportunity: embed AI directly into the design digital thread, putting manufacturability, pricing, sourcing and execution intelligence in front of engineers at the instant design decisions are made," says Randy Altschuler, co-founder and CEO, Xometry. "Xometry has built and trained its AI-native platform on the real-world complexities of manufacturing, including millions of part files, actual manufacturer feedback and production outcomes at global scale."

Xometry has built its platform on millions of part files, design feedback from real-world manufacturers and production outcomes across a global supplier network of more than 5,000 active suppliers. 

Siemens' customers will have a native integration workflow that will provide access to real-time feedback on design feasibility, manufacturing options, pricing and lead times directly within their existing design and lifecycle workflows.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

America Makes MMX is an annual member-centric event convening the additive manufacturing (AM) community for two days of high-level conversations, presentations, and panels.

MMX provides a platform for strategic discussion and content. The event also provides time for sharing the overall Institute accomplishments and where America Makes leadership envisions the organization going forward.

The event includes keynote speakers from government, academia, and industry. The conversations and presentations allow for information sharing and acknowledgment of AM advancements of AM to increase adoption of AM in the U.S. industrial base.

Individuals from industry, academia, government, and economic development agencies are encouraged to attend. MMX aims to unite the community, encourage collaboration and work towards building a competitive American AM industrial base.

Please note: Per CFR126.1, citizens of the following countries will NOT be allowed to participate in this event: Afghanistan, Belarus, Burma, Central African Republic, China, Cuba, Cyprus, Democratic Republic of Congo, Eritrea, Ethiopia, Haiti, Iran, Iraq, Lebanon, Libya, Nicaragua, North Korea, Russia, Somalia, South Sudan, Sudan, Syria, Venezuela, and Zimbabwe. Registration from these countries will be canceled.

For questions about registration or other event information, please email Zach Smith, Event Manager: zach.smith@ncdmm.org

To register, click here.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

“Modern manufacturers need to know if they’re making good parts. PC-DMIS FUSION modernises analytics and reporting with a simple, intuitive interface, interactive reporting, and fast access to historical data,” says Greg Holdsworth, senior portfolio manager at Hexagon. “Seamlessly integrated with PC-DMIS, it delivers real-time measurement information, visualization, and SPC analysis, all to increase productivity and support data-driven decisions.”

Integrated with PC-DMIS metrology software, Fusion presents measurement data captured from a range of devices, to unify shop floor data and drive automated quality workflows.

Connecting Devices and Automating Workflows

PC-DMIS FUSION streamlines data collection, reporting, and analysis. Users can automate repetitive tasks, receive immediate alarms when deviations occur, and monitor part quality continuously.

FUSION + MAESTRO

PC-DMIS FUSION pairs with MAESTRO, Hexagon’s all-digital, fully connected coordinate measurement machine and digitally-native industrial internet of things device, to create a fully automated metrology ecosystem. MAESTRO streams precise measurement data in real time, while FUSION interprets trends, automates SPC and reporting.

Together, MAESTRO and FUSION support virtual metrology and model-based manufacturing, measuring digitally, linking results to 3D models, and spotting issues as they occur. 

Key benefits of PC-DMIS FUSION include:

• Real-time metrology: Get information and statistical analysis and insights as they happen
• Modern user interface: view and interact with dimensional data in a modern and simple reporting format. 
• Usable insights: turn inspection data into usable insights for quality assurance and process improvement with alarms for monitoring parts or processes.

FUSION is available local Hexagon sales teams and authorized resellers. For more info, visit https://hexagon.com/products/pc-dmis-fusion

Sources: Press materials received from the company and additional information gleaned from the company’s website.

• Electrical equipment manufacturing in North and South Carolina: Siemens invested $165 million to expand two existing Siemens facilities and introduce three new sites across the Carolinas to support America’s AI and data center markets.
• Data Center infrastructure manufacturing in Fort Worth, TX: $190 million invested in a new 500,000-sq.-ft. facility to scale American production of critical electrical infrastructure, such as low-voltage switchboards.
• New passenger rail manufacturing hub in Lexington, NC: The $220 million greenfield manufacturing facility is building passenger rail coaches for the United States.
• Expanded electrical infrastructure manufacturing in Pomona, CA: The $95 million investment in a new greenfield manufacturing site and existing facility upgrades create a combined 146,000-sq.-ft. campus for low-voltage electrical equipment production.

A deep dive into Siemens’ U.S. investments can be found on our company blog. These investments are expected to introduce a total of more than 2,200 jobs by 2028. The roles span jobs in advanced manufacturing, skilled trades, engineering and logistics.

“Siemens has been helping to build and support America's industrial backbone as long as we've been a company, more than 175 years,” says Ann Fairchild, president and CEO of Siemens USA. “These investments—and those we anticipate in the years ahead—reflect our commitment to serving U.S. customers, the continued growth we see in the U.S. market, and our pride in supporting a strong, innovative domestic manufacturing base that is essential to America’s long-term competitiveness and resilience.”

Siemens facilities integrate the company’s industrial software and automation technologies to create production environments for artificial intelligence. Siemens’ U.S. manufacturing footprint also supports a network of more than 16,000 American suppliers.

Siemens’ new and expanded facilities in California, Illinois, Texas, Wisconsin, Pennsylvania, New York, and the Carolinas serve as a testament to energy-efficient manufacturing in the United States. Select carbon-neutral facilities, all-electric operations, on-site PV microgrid systems and integrated EV chargers all showcase how Siemens is progressing to be a net-zero carbon company by 2030.

For more information on Siemens in the USA, visit here.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

As one of a select number of companies accepted into the Autodesk Digital Twin Solution Provider Program, this marks a milestone in the firm’s progression from descriptive and informative digital twins toward predictive, enterprise scale twin solutions for transportation, facilities, and infrastructure clients.  Participation in the program validates Michael Baker’s ability to connect design, construction and operational data into a digital twin environment.

As a Digital Twin Solution Provider, Michael Baker will work closely with Autodesk to implement the Autodesk Tandem platform as a system of record for asset information for the firm’s clients, enabling owners and operators to establish a single source of truth across the asset lifecycle. 

“Joining the Autodesk Digital Twin Solution Provider Program positions Michael Baker to further accelerate how our clients adopt and scale digital twins across their portfolios,” says Rod Malehmir, Ph.D., chief technology officer at Michael Baker International. “Looking ahead, this partnership will support more connected project delivery, stronger continuity from design through operations and measurable improvements in how infrastructure and facilities are managed over time.”

Michael Baker has been a leader in digital twin strategy, platform integration and cross-practice workflows that align building information modeling (BIM), geographic information systems (GIS), reality capture, sensor data and analytics into cohesive digital environments.  Acceptance into the  Autodesk Digital Twin Solution Provider Program reflects the firm’s commitment to advancing owner-centric digital twin solutions that are scalable, standards-based and focused on operational outcomes, the company says.

“This program is an important pillar of our overall digital twin vision,” said Joseph Bartorelli, vice president, Digital Solutions at Michael Baker International.  “Autodesk Tandem plays a key role in how we partner with clients to establish a reliable digital foundation at handover and evolve that foundation into something that supports forecasting, optimization and decision support over the life of an asset.”

“Michael Baker International has been a valued Autodesk customer for years, and it’s exciting to see them expand their focus into digital twin-enabled building operations with Tandem. As they continue investing in this space, we’re looking forward to working together to help our collective customers turn fragmented building data into actionable business intelligence for smarter, more efficient operations,” says Hayli Hay, director of Digital Twin Partnerships at Autodesk.

Learn more about how Michael Baker is transforming infrastructure with digital twins: https://mbakerintl.com/services/digital-twins/

About Michael Baker International

Michael Baker International is a provider of engineering, consulting and technology services spanning four Verticals: Infrastructure, Integrated Design and Advisory (IDA), Mitigation, Environmental, Resiliency, Response and Recovery (MER3) and GovTech. The firm’s practices encompass all facets of infrastructure, including design and civil engineering for diverse bridge, highway, water, rail and transit and aviation projects, as well as planning, architecture, environmental and construction and program management.

To learn more, visit https://mbakerintl.com/.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

With the completed transaction, Cadline now operates as Mastercam Italia, expanding Mastercam's presence in the Italian manufacturing market.

This transition brings together Cadline' regional expertise with Mastercam's global technology leadership, enabling faster response times, more personalized service, and enhanced customer support for manufacturers throughout Italy.

"This marks an important milestone for Mastercam and for our customers in Italy," says Russ Bukowski, president of Mastercam. "By bringing Cadline fully into the Mastercam organization as Mastercam Italia, we are strengthening our local connections while delivering the full power of Mastercam's global resources."

The move from a channel partner model to direct operations enables Mastercam Italia to provide enhanced problem resolution, customized training programs, and regional specialists who understand challenges of Italian manufacturers. The acquisition supports Mastercam's growth strategy integrating innovation and targeted acquisitions.

As part of Sandvik's digital manufacturing portfolio, Mastercam is advancing connected manufacturing technologies and delivering support to customers worldwide.

About Mastercam

Mastercam, part of Sandvik Group, is a developer of CAD/CAM software for manufacturing, trusted by over 450,000 installations worldwide. Discover more at www.mastercam.com.

About Sandvik Group

Sandvik is a global, high-tech engineering group working with the manufacturing, mining, and infrastructure industries. Offerings include equipment, tools, services, and digital solutions for machining, mining, rock excavation, and rock processing. In 2025, the Group had approximately 42,000 employees.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

Manual Modeling to Intelligent Acceleration

AI Modeling Assistant acts as an intelligent co-pilot for CPQ modelers for CPQ-ready models. By ingesting inputs such as spreadsheets, technical specifications, PDFs, and legacy data, the assistant generates a structured model foundation up to 80% complete. Modelers then review, refine, and approve outputs before synchronizing directly into Tacton.

Built for Complexity of Manufacturing

Tacton AI is purpose-built for manufacturing and embedded within CPQ workflows. It understands the structure of configurable products, including assemblies, attributes, and constraints, and generates models within a governed environment where human validation is always required.

Driving Ongoing Scale

As manufacturers expand their portfolios and introduce new configurations, AI Modeling Assistant helps organizations accelerate updates, reduce manual effort, and increase modeling capacity across existing teams.

Availability

AI Modeling Assistant is available today in the Tacton platform. 

About Tacton

Tacton is a global leader in software for manufacturers of highly configurable products. Tacton delivers the Buyer-Centric Smart Factory, connecting buyer engagement with engineering and order fulfillment through a single source of truth. By uniting Configure Price and Quote, Configuration Lifecycle Management, and Configured Order Fulfillment, Tacton helps manufacturers manage complexity across the lifecycle.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

Morningstar CAM Solutions has been providing CAD and CAM software, training, postprocessors and technical support since 1986. With years of hands-on experience in the manufacturing sector, including owning and operating their own modern machine shop with three-axis, four-axis and five-axis machining centers, Morningstar is qualified to assist clients in simplifying, automating and accelerating their CNC programming processes with HCL CAMWorks software.

“We are thrilled to welcome Morningstar into the HCL CAMWorks reseller network. With their extensive experience in manufacturing and a proven track record of selling and supporting CAD/CAM software, I am confident they will prove to be a valuable resource for companies looking to drive machining efficiency and streamline their operations,” says Mark Cadogan, senior area sales director for HCL CAMWorks in the Americas.

“While there are plenty of other CAM software products on the market, we feel that CAMWorks truly stands alone with its ability to quickly recognize machinable features in a part and apply machining strategies from its technology database to them.  This reduces programming effort and ensures consistency within any machining organization,” says Tony Ledbetter, co-founder andpPresident of Morningstar.

HCL CAMWorks delivers a fully integrated CAD/CAM solution for CNC machining, with programming for 2.5-axis to 5-axis mills, lathes, mill turn, Swiss machining, wire EDM, and more.  By working within SOLIDWORKS and 3DEXPERIENCE SOLIDWORKS, there’s no need to maintain separate CAD and CAM files, as design and manufacturing data are stored in a single file.  When design changes occur, toolpaths and G-code update automatically.  With advanced features including automatic feature recognition, intelligence-based machining, and the TechDB for capturing best practices, programming time is cut, and machining cycles are optimized.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

By leveraging learned geometric representations of 3D models, the POC enables consistent, geometry‑aware similarity detection across large repositories, independent of how parts are named or categorized.

The Similar Parts Search technology demonstration combines Graph Neural Networks (GNNs) with advanced 3D geometric processing to enable similarity detection within large collections of engineering models. This approach allows the system to learn and leverage geometric characteristics of 3D parts.

By automatically preparing uploaded 3D models for analysis and generating persistent digital profiles for each part, the system supports scalable, reusable similarity searches across datasets. This foundation enables the technology to be extended into custom, production‑ready solutions that can integrate with existing CAD, PLM, ERP, or supply‑chain systems.

The demonstration leverages domain‑specific datasets, established expertise in geometric algorithms, and engineering model collections, including the Mechanical Components Benchmark (MCB)—an open‑source dataset developed by Purdue University and distributed under the MIT License.

The POC highlights how AI‑based geometric similarity search can deliver tangible operational and economic benefits across engineering, manufacturing, and supply‑chain domains. By enabling faster identification of previously manufactured or equivalent parts, Similar Parts Search supports quicker manufacturing cost and lead‑time estimation.

The prototype demonstrates the potential to reduce part proliferation and stock variation by minimizing near‑duplicate components across product lines. Improved similarity detection also contributes to lower warehouse and inventory holding costs through consolidation and reuse strategies, simplifies supply and procurement management via greater standardization, and enhances design reuse.

By pairing AI‑driven similarity metrics with intuitive visual validation, the demonstration supports human‑in‑the‑loop decision‑making. To see the current functionality of the Similar Parts Search technology demonstration, watch a short demo video.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

“More people than ever are designing, building, and running their own businesses. ... We’ve heard directly from our customers that they need more flexibility, more clarity, and technology that reflects how they actually work. Autodesk for Small Business is about building around their needs and building alongside them as they bring the world around us to life,” says Dara Treseder, chief marketing officer, Autodesk. 

To start, Autodesk for Small Business is focused on three areas:

Experience

Autodesk’s new Small Business Hub is a destination on Autodesk.com where small businesses can explore products, compare options, and find tools without navigating an experience designed for large enterprises, according to Autodesk. The hub also provides a direct line for users to share feedback. It’s live nowin the United States and United Kingdom, with more countries to follow later this year.

The company is also prioritizing product improvements shaped by small business feedback: 

• Design and Make: Autodesk Assistant in Fusion turns natural language into action across design and manufacturing workflows. It enables text-to-command modeling and automates workflows for transitions, while being contextually aware of the task at hand. 
• M&E: Autodesk Flow Studio has introduced flexible pricing tiers, including a freemium entry point, that scale with the needs of business so smaller studios and independent creators can experiment with the latest VFX and animation technology, without upfront costs. With the newest feature releases of Wonder 3D (a generative text to 3D model), AI Rigging, and Neural Layer, Flow Studio enables teams to streamline modeling, rigging, and animation. 

Flexibility

Autodesk Flex allows customers to prepay for token-based access to more than 100 Autodesk products, with no subscription and no long-term commitment.

Special offers

In May, Autodesk will introduce special offers; the company is also offering a free Autodesk Professional Certification exam voucher with the purchase of AutoCAD.

Recent Report

To understand how small businesses are shaping the future of Design and Make industries, Autodesk partnered with GlobalData on a new State of Small Business report.* 

Top findings include:

• Design and Make professionals are leaving corporate to start their own businesses: Nearly 1 in 5 Design and Make professionals (18%) say they’re considering starting their own business in 2026—six percentage points higher than the broader small business economy.
• Nearly 60% of small business owners in Design and Make say the latest technology is too complex or not designed for smaller teams—seven points higher than the broader small business economy.  
• Independent work is becoming the norm in Design and Make: More than one third of the Design and Make workforce (36%) now operates as freelancers or contractors, outpacing the broader workforce (32%) by more than three percentage points.

*Methodology

Autodesk’s State of Small Business report was developed in partnership with third-party analytics firm GlobalData. The analysis draws on GlobalData’s U.S. business and employment tracking data to evaluate growth trends in small businesses and freelance work over the past 3 years. 

Visit the hub. Check out the video. Download the report.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

The collaboration addresses discrete and process manufacturing's AI scaling challenge by combining enterprise-grade infrastructure with industry-specific capabilities.

"The conversation has changed from 'where do we start with AI' to 'how fast can we scale it," says Ozgur Tohumcu, general manager of Automotive and Manufacturing at AWS. "Through our collaboration with Infor, manufacturers are moving from pilot to production faster than ever — deploying industry-specific agents that deliver operational advantages at enterprise scale."

"Generic AI doesn't work in manufacturing — you need agents that understand manufacturing-specific operational processes, bill of materials, supply chains, and shop floor realities," says Rick Rider, senior vice president, Product Management, Infor. "AWS provides the enterprise infrastructure and AI horsepower, while we bring the deep industry-specific intelligence and context. The result is AI and agents built specifically for manufacturing industries that our customers can trust to run critical operations and deliver measurable financial impact."

Among Infor's complete collection of AI agents are those designed specifically to address complexities of manufacturing operations. Built on AWS infrastructure, Infor deploys agents across manufacturing's critical workflows with compliance controls and scaling from pilot to production:

• Project Management — Agents continuously compare baseline plans to real-time financials and performance metrics to protect margins, forecast revenue, and surface cost or schedule variances before they impact profitability.
• On-Time Project Delivery Management — Agents monitor project risks, milestones, dependencies, and cross-project performance to detect delays early and coordinate interventions that keep delivery on schedule.
• Process Mining & Operational Intelligence — Agents use process-mining-derived insights to automatically discover end-to-end workflows from event logs, surface bottlenecks and variant behaviors, and prioritize the highest-impact interventions so agents can close the loop with targeted remediation and continuous optimization.
• Inventory Flow Management — Agents track warehouse activity, stock movements, and inbound and outbound orders to maintain real-time inventory visibility and ensure materials move through the supply chain.
• Financial Operations Management — Agents connect contracts, billing, supplier invoices, and general ledger activity to automate financial oversight, streamline processes, and provide a unified view of financial performance.
• Quality Management — Agents monitor inspections, non-conformances, and material disposition across orders and projects to identify deviations and maintain consistent product and operational quality.

Customers can also build and deploy their own custom agents through Infor Agent Factory and in conjunction with Infor AI agents through orchestration, using Amazon Bedrock AgentCore Amazon Bedrock, and Amazon SageMaker.

Availability

The solution is currently in limited availability and available to customers through Infor Velocity Suite, an all-inclusive package of AI solutions, tools, and expertise for Infor CloudSuite customers across Distribution, Industrial and Process Manufacturing, and related verticals. Visit infor.com/partners/aws to get started.

About Infor
Infor is a global leader in business cloud software specialized by industry. 

Sources: Press materials received from the company and additional information gleaned from the company’s website.

The conference is an opportunity for knowledge sharing and networking with industry peers and leaders from a diverse range of sectors, including automotive, aerospace, energy & power, consumer products, process manufacturing, life sciences, and everything in between.

The Location

NAFEMS will be in Vancouver, at the Vancouver Convention Centre, just 25 minutes from Vancouver International Airport.

Call for Presentations - Opens June 2026

Stay tuned for full details on registration, abstract submissions, and sponsorship opportunities at nafems.org/congress

Sources: Press materials received from the company and additional information gleaned from the company’s website.

"We're excited to partner with Dassault Systèmes to bring the power of Coreform IGA to the Abaqus community," says Matthew Sederberg, CEO of Coreform. "This partnership is a significant step toward making advanced simulation more accessible and efficient for engineers working on complex, real-world geometry."

Coreform has developed technology that lets simulations run directly on fully-featured CAD geometry. 

The Coreform IGA for Abaqus add-in extends Abaqus workflows by allowing engineers to run Abaqus/Standard directly on CAD geometry,  without defeaturing or manual model preparation. The result: faster product design cycles, improved simulation fidelity, and less time and specialized expertise required to get from CAD to a solution. It's Abaqus driven directly from CAD without traditional meshing operations.

"This partnership isn't just good news for Coreform," says Greg Vernon, Coreform's director of Engineering. "It marks a significant step forward for CAE  toward workflows where engineers can run high-fidelity simulations directly on the CAD geometry their colleagues designed."

Coreform IGA for Abaqus is currently in development. Abaqus users interested in early access are invited to apply for the beta program, join the waitlist, or explore how the add-in can simplify their simulation workflows.

To learn more, contact info@coreform.com or visit www.coreform.com. For more information about the Simulia Integration Partner program, visit www.3ds.com.

About Coreform Inc.

Coreform develops next-generation computer-aided engineering software. Coreform’s founders are authorities in isogeometric analysis. The Coreform IGA solver is based on CAD spline geometry. Coreform is also the exclusive commercial distributor of the Cubit meshing software. 

Sources: Press materials received from the company and additional information gleaned from the company’s website.

Neuromorphic processors are not a replacement for GPUs or NPUs, according to JPR; instead, they carve a new subsegment in device inference and IoT, where always-on sensing and sub-milliwatt power budgets make conventional AI silicon impractical. The JPR report covers the architecture, competitive landscape, and commercial trajectory of spiking neural network (SNN) chips from BrainChip, Innatera, SynSense, Intel, IBM, and a new generation of start-ups including IMChip and Rayd Technologies.

The chip-only market sits at $87–$340 million in 2025, growing at a 50%–52% CAGR to reach $3.3–$4.1 billion by 2031–2034. IoT applications account for 45% of forecast revenue, device inference 30%, and autonomous systems 20%. Blended ASP compresses from $45 in 2025 toward $9–$12 by 2031 as consumer wearables and hearables drive volume. Unit shipments climb from 3–8 million in 2025 to more than 100 million by 2031.

“Neuromorphic chips solve a problem that GPUs cannot address economically — persistent, always-on intelligence in devices that run on a coin-cell battery for years. The software ecosystem is still immature and benchmark standards do not yet exist, but the physics argument is compelling. We expect this segment to compound faster than mainstream AI silicon through the end of the decade," says  Jon Peddie, president, Jon Peddie Research.

The report includes competitive profiles, an SNN supplier table, market size and unit shipment forecasts through 2031, ASP analysis by segment, and an inflection signal assessment evaluating whether neuromorphic adoption represents a structural shift in AI hardware procurement.

Pricing and availability

JPR's Neuromorphic AI Processors report is available now to JPR subscribers and for individual purchase ($300) at jonpeddie.com. Also, there is a bundle opportunity with the massive 357-page Annual AIP Market Development report and the new AIP Tracker Service.

The work combines Dallara’s expertise in high-performance vehicle engineering with IBM’s leadership in AI for physics and quantum computing to investigate how to accelerate aerodynamic design and open a path to even more advanced simulation workflows.

For more than 50 years, Dallara has designed and supplied high-performance vehicles for some of the world’s top racing series, including IndyCar as well as Formula 2, Formula 3, Super Formula, and Indy NXT, with additional work in top-tier series such as Formula E, WEC, and IMSA.

These racing programs provides ability to validate simulation results against real-world vehicle performance. Dallara also applies its engineering to high-performance road vehicles and aerospace. 

As part of the project, IBM has been developing domain-specific foundation models in close coordination with Dallara. The models leverage Dallara’s high-fidelity aerodynamic simulation data and the company’s technical expertise. Teams also aim to integrate validated measurements of real vehicles in wind tunnels and on the track.

IBM and Dallara are using AI to speed up those workflows without replacing physics. In one early example, which focused on the geometry of a conceptual Le Mans Prototype 2 (LMP2)-like race car, the two companies jointly compared CFD analyses of multiple configurations of the rear diffuser with results from the new physics-based AI method.

The traditional approach took a few hours to calculate all the configurations. Meanwhile, the AI model completed the same evaluations in about 10 seconds, identifying the same optimal design with roughly the same error margins as CFD, according to IBM and Dallara. 

These and other preliminary results suggest Dallara engineers can evaluate more vehicle configurations in a fraction of time to move faster in early design phases.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

The new committee (F50) will focus on frameworks, best practices, and standards for AI technologies in manufacturing.

“Artificial intelligence is transforming manufacturing globally,” says Jay Lee, Ph.D., Clark distinguished professor and director of industrial AI center, University of Maryland, College Park. “Establishing trusted standards through ASTM will be essential to ensure that AI-driven manufacturing systems are reliable, interoperable, and scalable across industries. Our goal is to bring together industry, academia, and government to establish practical and globally relevant standards for AI-enabled manufacturing.”

The committee will convene stakeholders from across the manufacturing industry to advance work in the following areas:

• Establishing definitions and classifications for AI concepts, applications, and methods as they pertain to manufacturing
• Developing standards for how manufacturing data is collected, handled, shared, and protected
• Guiding integration of AI systems with factory equipment and software to enable interoperability at scale 

“The establishment of ASTM’s new artificial intelligence in manufacturing systems committee marks a pivotal step forward for the industry,” says Andy Kireta, ASTM president. “ASTM standards, developed through our robust consensus-driven process can help drive innovation, enhance quality, and empower safety in AI-driven manufacturing systems.”

The inaugural meeting of the committee will take place June 3-4, 2026 during ASTM's committee week at the Hyatt Regency in Dallas, TX. The event will consist of a full-day knowledge sharing event followed by a day of facilitated standards development work sessions. To attend, visit https://na.eventscloud.com/website/86868/

The ASTM standards development process is open to anyone with an interest; to join the new committee and participate, visit go.astm.org/join. 

The work of the committee will support U.N. Sustainable Development Goal #9 on industries, innovation, and infrastructure.  

Sources: Press materials received from the company and additional information gleaned from the company’s website.

Dyad brings autonomous AI agents into the digital design and testing of industrial machines. From heat pumps to satellites to semiconductors, engineering teams can compress cycles of design, testing, and building from months to minutes, accordingt o JuliaHub. Several Fortune 100 companies are already leveraging Dyad and Julia across several industrial sectors such as aerospace and automotive.

“Systems modeling is one of the most strategically important layers of the AI-native engineering stack, because it is where physics, control logic, and AI converge," says Daniel Freeman, who led the Series B round for Dorilton Capital. "JuliaHub has built something extraordinary with Dyad: a platform that doesn’t just model systems, but compiles them, taking engineers from concept to production control code in a single environment.”

Dyad gives engineering teams an AI-first environment to model, test and validate industrial systems. Dyad 3.0 builds on Dyad 1.0 (launched in June 2025) and Dyad 2.0 (launched in December 2025). Dyad connects autonomous agents with scalable physics simulations, rigorous controls, safety analysis, and the ability to generate code for embedded systems.

“It’s not about helping engineers complete one small task at a time. It’s agentic engineering at scale, where teams can feed a full specification to Dyad and have it design the complete system. Spec in. Design out,” says Viral Shah, CEO of JuliaHub.

Dyad’s cloud-based agents are designed to scan through the scientific knowledge to constantly improve models. AI-automated lab testing is to ensure models match physical reality. Dyad’s simulation ecosystem and language offer a foundation on which all of these learnings are relayed back to engineers to check the processes, determine whether assumptions match customer requirements, and be the human in the loop that ensures the safety of the final product. 

“Dyad is transforming system-level engineering by combining scientific AI, agentic modeling, and a powerful compilation pipeline into a unified workflow," Prith Banerjee, senior vice president of Innovation at Synopsys.

Integrated with Synopsys simulation software Ansys TwinAI, it enables high-fidelity hybrid digital twins by integrating physics-based simulation with data-driven models. 

Dyad’s modeling language is purpose-built to be easy for AI agents to understand, according to JuliaHub. Its foundational logic is grounded in the laws of physics, allowing its agents to reason about how fluids move through machines, how wind speed and temperature affect components, and how fundamental forces like gravity shape design.

Dyad 3.0 Launch event

Dyad 3.0 will be officially unveiled at a live event on May 19. Join us to see live product demonstrations.

About JuliaHub

JuliaHub was founded in 2015 by the creators of Julia, the high-performance open-source language developed at MIT and now used by over a million developers worldwide. JuliaHub combines advanced mathematical computing and machine learning expertise to enable scientific machine learning techniques, Digital Twin solutions, and next-generation modeling and simulation in aerospace, automotive and other industrial verticals.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

The first project call, Maturation Initiative for Additive Metals Interchangeability (MIAMI), is worth $12.4M and is funded through the Office of the Under Secretary of War, Acquisition and Sustainment, Industrial Base Analysis and Sustainment (IBAS) Program. This opportunity validates that metallic additive manufacturing (AM) materials can replace traditional alloys in Department of War (DoW) weapon system components.

Project teams will select candidate parts, define performance requirements, and generate shared, validated data demonstrating that the AM material meets or exceeds properties of the legacy alloys it is intended to substitute. The goal is to enable cross-platform use of AM materials, reduce redundant testing, and accelerate qualification so AM solutions can be adopted quickly and confidently across the defense industrial base. 

“Advancing material interchangeability through additive manufacturing is a strategic step toward strengthening the nation’s defense posture,” says John Martin, Additive Manufacturing research director at America Makes. “This effort delivers the analytical rigor and validated data needed to accelerate trusted AM adoption, directly supporting the Department of War’s priorities for a more resilient and responsive industrial base.”

The MIAMI project call consists of two phases, beginning with Phase 0. This phase will provide a documented assessment of applications and traditional materials where the selected AM material is expected to serve as a substitute.

Planning: Define qualification-ready process, material, and feedstock specifications and provide a analysis identifying where the selected AM material can replace legacy components across a weapons system, including priority use cases, performance requirements, and adoption conditions.

Process definition: Develop process control documentation.

Preliminary data: Conduct preliminary qualification testing to confirm that the defined process controls produce AM material with room‑temperature mechanical properties that meet established threshold requirements.

Phase 0 work will be followed by a final phase in which testing and demonstration will be conducted according to the test plan developed in Phase 0.

The second project call, INtegrated System for In-situ Testing & Evaluation (INSITE), worth $13.2M, is funded through the Office of the Under Secretary of War, Acquisition and Sustainment, IBAS Program, and the Office of the Under Secretary of War, Manufacturing Technology Office (OSW ManTech). 

The project’s objective is to establish an integrated AM quality‑assurance system that unifies in‑situ monitoring and post‑build inspection.

This combined in-situ and post-build nondestructive evaluation (NDE) approach aims to improve inspection of some of AM’s challenging parts. By combining real-time monitoring during production with accelerated post-build inspection and expert oversight, the approach seeks to strengthen quality assurance, support certification, and expand manufacturing capabilities for critical components.

“As additive manufacturing scales to larger and more complex components, the ability to confidently verify part quality becomes mission critical,” says Ben DiMarco, Technology transition director at America Makes. “This project brings together advanced analytics, in‑situ monitoring, and next‑generation NDE into a unified strategy that strengthens our industrial base and accelerates the deployment of reliable AM capabilities across defense applications.”

The INSITE project consists of tasking phases with associated decision gates for each, including:

• Gate 1: Initial integration of sensors into the AM machine and collection of in-situ data.
• Gate 2: Demonstration of in-situ data identifying areas of interest for post-build NDE.
• Gate 3: Correlation of in-situ data with NDE results.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

AMD leaders will join Chair and CEO Dr. Lisa Su, alongside AI ecosystem partners, customers and developers, to share how the company’s end-to-end AI solutions, from silicon to software, are reshaping the AI and high-performance computing landscape.

The event will be hosted in-person on Thursday, July 23 and livestreamed on the AMD YouTube channel.

About AMD
AMD focuses on high-performance and AI computing to solve challenges. AMD technology powers billions of experiences across cloud and AI infrastructure, embedded systems, AI PCs and gaming. With a portfolio of AI-optimized CPUs, GPUs, networking and software, AMD delivers full-stack AI solutions that provide the performance and scalability needed for a new era of intelligent computing. 

Sources: Press materials received from the company and additional information gleaned from the company’s website.

 

The group has been creating reports and benchmarks for several years, including reports on topics like the requirements for simulation software interoperability, for example.

This year there were several sessions focused on determining the business value of simulation, and then communicating that effectively to upper management to align simulation activities with business goals. 

Darrel Meffert, who is responsible for enterprise-wide simulation strategies at Caterpillar, gave a great talk about how the company has for decades leveraged simulation to help reduce physical testing, save money, and improve quality. In 2013, the company launched its enterprise simulation strategy to increase use of the technology and give design leaders the confidence to make decisions without physical testing. Implementing that strategy successfully took focus and determination, and the ability to clearly communicate value across different parts of the company.

Mike Henneke of the Koch Engineering Solutions’ business John Zink Co. also discussed how simulation has helped speed time to market and reduce the need for physical testing of its burner systems (which are deployed in refineries). The company has made a deliberate effort to tie simulation efforts to business outcomes. He also discussed different frameworks his team uses to determine the desirability, viability, and feasibility of new products and designs.

Consultant Brian O’Keefe of Articulus also gave a presentation on how to create presentations to sell value that executives understand. You can learn more about ASSESS here.

It’s been a busy travel season here at DE. In addition to ASSESS, we’ve been to NVIDIA’s GTC conference, the recent Synopsys event, and AMUG (Additive Manufacturing Users Group) in Reno, but we were snowed out of the Additive Manufacturing Strategies event in New York in February—not to worry, though; we will connect with our 3D printing friends at RAPID later this month.

We hope to see you at other upcoming shows, and that you enjoy the current issue.

“Engineering intent is the missing layer in digital transformation,” says Andre Wegner, CEO of Authentise. “We’ve spent 14 years helping companies digitize workflows. Whisper is the next step. It doesn’t ask engineers to change how they work. It listens, understands, and acts.”

Whisper delivers gains in compliance, coordination, and execution. Whisper captures engineering activity as it happens across tools like Slack, email, meetings, and enterprise systems. It structures that data, applies context and permissions, and acts on it directly within existing workflows.

Results include earlier risk detection; real-time compliance checks; automated updates across enterprise resource planning, product lifecycle management, and quality management systems; and full provenance and audit trails tied to parts and projects.

A Platform, Not a Feature

Whisper is not a single application but a foundation for building intelligent engineering workflows. It enables real-time compliance monitoring, project health detection, and automatic updates across enterprise systems, all driven by configurable agents operating in the background.

Example use cases include:

• Real-time generation of technical documentation
• Updating task lists, PLM records, and ERP data automatically

Open, Extensible, and Source-Available

Whisper is released as source-available, allowing ability to extend and deploy it within unique environments.

Initial access requires a low upfront commitment, with full costs only incurred once value is determined.

Early access programs available immediately for select partners and customers. Find out more information on www.authentise.com/whisper.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

Version 24 update delivers enhanced performance for the application program modeling finite element method application including added capability for working with materials from the Materials and Assortments Library, along with integrated help documentation, and localization improvements. KompasFlow and 3D Shafts & Mechanical Gears applications have been also updated.

In addition, help documentation has been added for several Equipment applications, including Steel Structures 3D, Pipelines 3D, and 3D Model Recognition, while overall operational performance has been increased.

To explore updated KOMPAS-3D v24 request a free trial.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

Windchill AI Assistant enablesusers to ask questions in plain language and receive contextual answers or summaries based on the content of Windchill documents. It helps users review lengthy files, surface relevant details, and access insights embedded in years of engineering tests, reviews, and technical documentation.

All responses reference the source of information, grounding each response while ensuring that access control rules are enforced. The capability is deployed as a plugin, allowing customers to adopt new AI capabilities.

“For many customers, the challenge isn’t a lack of product data. It’s how difficult it can be to find and reuse what teams have already learned across past engineering work,” says John Haller, general manager of Windchill, PTC. “With Windchill AI Assistant, we’re applying AI in a practical way to help teams get faster access to trusted information already in Windchill, so they can spend less time searching for answers and more time applying insights to their work.” 

PTC plans to advance Windchill AI Assistant by expanding how users interact with Windchill data. Future enhancements may include adding AI agents across additional product domains, including parts and change management, and embed AI‑driven actions directly into workflows. PTC also plans to incorporate process and domain knowledge to help guide users as they complete tasks in Windchill. 

Windchill, along with the rest of PTC’s portfolio, supports the company’s vision for the Intelligent Product Lifecycle. Working alongside other AI solutions, such as PTC’s Creo AI, Codebeamer AI, ServiceMax AI, Onshape AI, and Arena AI, Windchill AI helps organizations scale AI adoption. 

For more AI innovations across PTC’s portfolio, visit the AI in Focus Executive Series. 

To learn more about Windchill and Windchill AI, visit https://www.ptc.com/en/products/windchill/windchill-ai.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

With GENESYS 2026, Zuken expands access through SIDEKICK, a web-based collaboration client that allows a greater ranged of users to work with model-based information.

SIDEKICK provides a browser-based environment where reviewers, project managers, and domain specialists can access model-derived information, track responsibilities, and participate in structured workflows without requiring full desktop access. This release also improves how users organize and act on assigned work, Zuken shares.

GENESYS 2026 also introduces performance improvements across core modeling workflows. Updates to diagram loading, navigation, cross-project performance, project open times, and report generation help teams work with large, interconnected system models.

In addition, the release enhances the modeling experience with updates to diagramming, reporting, and usability. New options for diagram layout and formatting give users more flexibility in creating and interpreting model views, while reporting enhancements support faster generation and sharing of model-based information. 

“GENESYS 2026 focuses on addressing common challenges in scaling model-based systems engineering across complex development environments,” says Andy Henderson, principal product manager for GENESYS at Zuken. “By improving performance, accessibility, and usability, this release supports teams working with large system models and extends access to model-based information across functions.”

Availability
GENESYS 2026 is available now. For more information, visit Zuken’s website. 

Sources: Press materials received from the company and additional information gleaned from the company’s website.

Blykalla is developing lead-cooled fast reactors designed to deliver safe, scalable, and carbon-free baseload power, according to the company. As Blykalla advances its next-generation reactor technology, it says a digital foundation is needed to manage increasing engineering demands and long-term lifecycle data.

“Developing advanced nuclear systems requires rigorous control over requirements, configuration, and change across the entire lifecycle,” says Michaela Casarella, configuration manager at Blykalla. “Aras Innovator provides the flexibility and visibility we need to manage this complexity while supporting our long-term vision for scalable SMR deployment.”

Aras Innovator will serve as a central digital backbone, bringing together product and plant lifecycle data to manage requirements, designs, assets, and documentation in a unified environment. Built on an open, flexible architecture, it enables Blykalla to advance its systems engineering and configuration management objectives, align with industry standards, and integrate with existing engineering and simulation environments. The platform supports a scalable digital thread across disciplines.

“Blykalla is building a standards-aligned, future-ready digital environment designed to evolve alongside its SMR program,” says Leon Lauritsen, CEO of Aras. “Aras Innovator provides the foundation to connect product and plant lifecycle data from early design through operation, enabling the traceability, collaboration, and control required to manage complex, highly regulated systems.”

With Aras Innovator, Blykalla aims to strengthen collaboration across engineering teams and mitigate risk associated with fragmented data and configuration errors. 

About Blykalla
Blykalla is focused on advanced nuclear technology, commercializing lead-cooled fast reactors for industrial use. Based on over 25 years of research, the company's SEALER technology is a compact 55 MWe reactor designed to offer a safe, scalable power solution. The OECD Nuclear Energy Agency's annual SMR dashboard recognizes Blykalla as having one of the most mature advanced reactor concepts in Europe. 

Sources: Press materials received from the company and additional information gleaned from the company’s website.

With funding from the UK Ministry of Defence, the cell utilizes advanced additive manufacturing technology to manufacture critical components for next-generation aircraft engines, according to Rolls-Royce. By integrating this German-engineered technology, UK capability remains devoted to aerospace engineering, manufacturing complex parts with reduced lead time, reduced cost, and increased efficiency, the company adds.

The machinery will play a role in advancing innovation, knowledge and skills across the Global Combat Air Programme (GCAP) and future combat power and propulsion, across Defence and wider Rolls-Royce applications, Rolls-Royce shares. With new optimized, lightweight engine components produced, future aircraft will benefit from more power and better fuel efficiency, the company adds.

"Across Rolls-Royce we are committed to the continued development and enhancement of our technologies to meet the demands of today, while providing scalable infrastructure required for the future," says Andy Higginson, senior vice president of Manufacturing, Assembly and Test, at Rolls-Royce. "Programmes like FCAS and GCAP will be fundamental to the UK’s future aerospace sector and capabilities like additive manufacturing will be crucial to enabling innovation at pace, driving cost savings and enhancing the skills and capabilities of our people.”

With engineers being specially trained to use the cell, the advancement in technology will help to sustain and create jobs at the Bristol hub that continues to be the home of UK military combat and transport aerospace power and propulsion, according to Rolls-Royce.

Housed in a custom-built, controlled 350 m2 space, the humidity, temperature and air pressure are optimised to ensure consistent quality. The AM machinery manufactures aerospace components layer by layer using metal super-alloy powders—constructing complex metal components by intricately melting layers of powder using laser beams.

Discover more about AM at Rolls-Royce Additive Layer Manufacturing | Rolls-Royc

Sources: Press materials received from the company and additional information gleaned from the company’s website.

This latest release delivers expanded format support, enhanced product manufacturing information (PMI) handling and new capabilities for complex geometry processing. Designed to improve efficiency across CAD translation, modeling, meshing, and simulation processes, the latest updates introduce expanded format support and more.

Some highlights include the following:

3D InterOp

NX Reader Enhancement for 2D Drawings

The NX reader imports 2D drawings as visualization data from NX 2412 and later versions.

glTF Writer Supports Draco Compression

glTF export incorporates Draco compression for meshes and point-clouds to significantly reduce output file sizes.

Enhanced Support for Reading Product Manufacturing Information (PMI)

• The STEP Reader supports graphical PMI at the assembly level.
• The JT Reader supports semantic PMI at the assembly level.
• The NX Reader supports PMI created in drafting mode when attached to a solid (PMI attached to drawing sheets are excluded).

Updated CAD Format Version Support

• JT 10.11
• NX 2512
• Solid Edge 2026

Data Prep

Hidden Body Removal (HBR)

HBR extends support for assembly models within UConnect workflows to several CAD formats like Solid Edge and Revit as well as for mesh formats like 3MF, OBJ and Smart 3D.

3D ACIS Modeler

Blending (Filleting) Enhancements

• Rho-Conic Cross-Sections: Create blends with conic cross-sections defined by a rho-parameter.
• Enhanced robustness when simultaneously processing multiple edge sequences: Improves stability and prevents overlaps and interference between blends.

Resolution of Edge Self-Intersections

Enable checking and optional splitting of edges with self-intersections.

For more product details, click here.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

The new Simulink Copilot supports model-based design, while Polyspace Copilot improves embedded software code analysis, which the company says can help engineering teams improve productivity while “maintaining rigor, traceability, and repeatability in their designs.” The new release also includes additional updates that help engineers design faster, fix issues earlier, and move more efficiently from development, through verification, to production. 

According to MathWorks’ Senior Principal Technologist Jason Ghidella, the need for these types of AI features is driven by the increased engineering complexity, which can make it difficult for new team members or stakeholders to get up to speed on these complex models. “Simulink Copilot helps you undnerstnd and work with these models quickly,” he says. “As you open the model it can give you direct advice about the structure, or what block to go to if you are lookign for a particular subsystem.”

The Polyspace Copilot also helps with onboarding, while also improving productivity without compromising the required rigor of embedded code analysis. 

According to a company press release, MathWorks is embedding copilots directly into the environments engineering teams are already using, such as MATLAB Copilot, Simulink Copilot, and Polyspace Copilot. At the same time, MathWorks has integrated MATLAB and Simulink functionality into agentic workflows through MATLAB MCP Core Server and MATLAB Agentic Toolkit. The company says this allows teams to understand designs faster, address software issues earlier, and apply development and verification workflows more consistently.  

“Engineering teams now have access to capabilities enabled by generative AI, and leaders need confidence that these translate into tangible engineering and business benefits,” said Avinash Nehemiah, Head of Product Management and Marketing, Design Automation at MathWorks. “In engineering design and software verification, productivity improvements cannot come at the expense of rigor, traceability, or trust. MathWorks is committed to delivering grounded AI tools for engineering that help teams move faster while preserving the discipline and confidence required to develop complex engineered systems.”

MATLAB Agentic Toolkit equips AI coding agents with expert knowledge and a live MATLAB connection to write idiomatic code, test and debug it, build apps, and use the full range of MATLAB toolboxes across evolving agent platforms. Image courtesy of MathWorks.

Simulink Copilot provides guidance tailored to the engineering context teams already use, the company says. The product can generate model explanations, answer questions about model behavior, and help users locate relevant blocks and subsystems in their models. By isolating issues, suggesting remedies, and guiding next steps, the copilot helps engineers move design work forward more quickly. Engineering teams can also use it to execute standardized tasks that support more consistent development and verification practices.  

R2026a introduces Polyspace Copilot and Polyspace as You Code. Polyspace Copilot provides guidance based on Polyspace analysis results to help engineers interpret static analysis findings, understand issues, and resolve them more efficiently. Polyspace as You Code enables developers to check C and C++ coding rules and identify coding defects and vulnerabilities as code is written, including code produced with AI-assisted tools. Together, these offerings help teams identify issues earlier and strengthen software quality throughout the development lifecycle.

“The big difference between Simulink Copilot and generic AI tools our customers might be familiar with is that we are reading the model structure and using MathWorks documentation for tha guidance,” Ghidella said. “It also opens up within the model itself. There is no context switching, which minimizes errors. With the Polyspace Copilot, the solution  is grounding its responses based on the results of the Polyspace formal analysis engine. The copilot can identify a defect and then explain why the problem has been identified as a defect, and then helps you resolve the issue.”

R2026a also includes three enhancements across the Polyspace product family: a new Polyspace desktop application for unified configuration and results management, extensions to Polyspace Bug Finder with custom checkers and coding standards, and software-sanitizing capabilities in Polyspace Test for dynamic analysis of runtime errors. Together, these enhancements provide a more unified workflow for software quality activities across development, testing, and verification.

R2026a’s updates across the MATLAB and Simulink product families include:

• MATLAB Course Designer: A new product that helps educators develop courses, courseware, labs, and assessments using MATLAB and Simulink.

• Simulink FMU Builder: A new product that creates standalone Functional Mockup Units from Simulink models and C or C++ code to support model exchange and integration workflows.

• MATLAB: Build and share interactive webpages with visualizations without installing MATLAB. Engineers can also manage Python environments and improve data exchange between MATLAB and Python workflows.

• Simulink: Access commonly used actions more efficiently through simplified, task-focused context menus. Users can also simulate C and C++ code within models without language limitations or additional wrappers.

• Wireless Network Toolbox: Model, simulate, analyze, and visualize wireless communication networks to evaluate end-to-end system behavior.

• MATLAB Test: Generate starter tests, equivalence tests, and tests from command history using MATLAB Copilot. Engineers can also run tests related to the current file to reduce unnecessary execution.

• Mapping Toolbox: Enhance geospatial analysis with 3D building visualization, image overlays, and raster map capabilities.

• Signal Processing Toolbox: Design and analyze digital filters with the new Filter Designer and Filter Analyzer apps, label time-frequency data, and extract signal features using enhanced interactive tools.

You can learn more about the new update here.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

Looking at the AM market as a whole, some market reports put the global AM market size at $67 billion-plus in 2026, with expectations of growth to exceed $441 billion by 2035 with an estimated CAGR [compound annual growth rate] of ~23% from 2026 to 2035, according to Business Research Insights. In instances where AI/IoT is integrated with AM, the results to date translate to improved production, enhanced process automation and quality control, among other factors, the market report adds.

We talked to several vendors in the software and hardware space for their perspectives.

Authentise knows AI—its software is driven by it. The company, doing business since 2012, develops tools for data-powered workflow management and process automation in additive manufacturing. Its tools, Threads, Flows, and Digital Design Warehouse, were created with a goal to optimize the 3D printing production lifecycle, according to company founder and CEO, Andre Wagner. 

“We build software for additive manufacturing that uses AI to solve practical, high-value problems rather than AI for its own sake,” Wagner explains.

For example, in reverse engineering, he explains that the Authentise Threads software uses AI to “help stitch together fragmented drawings, generate bills of materials, investigate missing information, and draft Technical Data Packages” The Flows software also integrates AI to “extract structured data from documents such as test results, which reduces manual entry and improves traceability.”

At Materialise, a 3D printing software and services developer, Bart Van der Schueren, chief technology officer, says the company uses AI for production quality assurance and to improve engineering productivity and automation.

“On the production side, in CO-AM Quality & Process Control we apply AI to analyze in-situ process monitoring datasets (layer images and other sensor inputs),” Van der Schueren says. “This makes it feasible to interpret complex, high-volume build data consistently, helping move ISPM [in-situ process monitoring] from a research capability into advanced in-process quality control and potentially reducing reliance on post-print inspection.

Materialise CO-AM quality and process control applying AI to analyze in-situ process monitoring datasets. Image courtesy: Materialise

He further explains how Materialise views large language models as becoming “a practical ‘copilot’ layer, especially in tools that support scripting and workflow automation.” For example, for Materialise solutions like CO-AM Brix, natural language can allow for users to develop or change workflows by “driving the right API calls, with appropriate guardrails.”

Arvind Rangarajan is global head of Product and Strategy at HP Additive Manufacturing Solutions, a developer of hardware, materials, and services. He shares how at HP, AI already is built into several stages of the AM workflow: design, build preparation, production optimization, and quality assurance. 

“Our AI Text-to-3D technology lets users turn simple written prompts into ready-to-print models, cutting design time for complex geometries. HP 3D Build Optimizer also analyzes build data to refine packing, anticipate risk areas and improve both throughput and unit cost,” Rangarajan explains.

AI and Adaptive Manufacturing

As use of AI in AM becomes practically second nature, there may be a shift emerging, with AM moving toward a more intelligent, adaptive manufacturing process.

Materialise’s Van der Schueren notes that AI is already moving AM from mostly a manual, expert-driven workflow to a more data-driven, adaptive production approach.

“In production, AI enables more objective interpretation of in-process signals and faster decisions about anomalies and defect criticality,” Van der Schueren explains. “In engineering operations, LLM copilots can reduce friction in building automation: instead of searching through menus or writing boilerplate code, users can express intent in natural language and have the system generate executable actions through APIs [application programming interfaces].”

His prediction is that traditional user interfaces won’t disappear, but he suspects the future will have more of a hybrid approach. “Copilots help users reach a first workable result quickly, and then engineers refine, still applying domain knowledge and adjusting parameters as needed,” Van der Schueren adds. 

HP Additive Manufacturing Solutions has three focus areas when talking about use of AI: ease of use, productivity and manageability. 

“By analyzing sensor data and print-process variables, AI enables more accurate parameter tuning, faster root-cause detection, inventory management, and seamless support,” he shares. “This reduces TCO [total cost of ownership], improves repeatability, and reduces the need for manual intervention. Over time, these capabilities support more predictable production output, smoother scaling and better integration with wider digital manufacturing systems.”

At Authentise, Wagner suggests current use of AI is only the beginning of the road toward adaptive manufacturing, “Today, AI is mostly being applied to specific point problems, such as visual inspection, data extraction, or documentation.

“Over time, the bigger shift will be AI managing context across the full additive workflow, connecting data and decisions from design through production, post-processing, inspection, and documentation,” Wagner adds. “That will make the process more intelligent overall and allow downstream steps to adapt based on upstream insights. For example, what happens in printing should increasingly inform how a part is finished, inspected, or approved later in the process.”

Trending Factors

To date, certain trends surrounding use of AI in additive manufacturing may prove more beneficial than others. The experts rank their list of trends.

Reverse engineering and design intent capture (or the embedding of design requirements and manufacturing constraints into the digital 3D model) top Wagner’s list of potential trends regarding AI. 

He expounds, “If we can use AI to understand not just the geometry of a part but the intent behind it, we can begin to connect that intent to other AI systems in design automation, process selection, documentation, and ultimately qualification. That opens the door to much faster and more scalable redevelopment of parts across different manufacturing routes. It will take many systems working together, and strong human oversight, but the direction of travel is clear.”

HP’s Rangarajan sees the combination of AI-powered design generation with connected digital manufacturing platforms as a long-lasting influential trend. “AI-enhanced design tools can help engineers create optimized geometries more rapidly, including simulation-driven iterations that previously required niche expertise. 

“Another trend is the rise of scalable digital manufacturing networks in which AI supports distributed production, quality assurance and fleet-level consistency. This will improve the acceptance of additive as a true manufacturing modality,” he adds.

A hardware piece from Materialise Control Platform, an open, machine-embedded, hardware- and software-driven platform that gives real-time management over laser-based 3D printing systems. Image courtesy: Materialise

Van der Schueren of Materialise details several specific trends to watch: 

• Multi-sensor, multi-source correlation for higher confidence in decision making, especially for critical parts.

• LLM copilots that can take actions (not just answer questions). He gives an example of generating scripts or creating workflows via application programming interfaces in automation-driven solutions (such as CO-AM Brix).

• Orchestration/guardrails layers (model context protocol-style patterns) that “provide context, constrain actions, and ensure traceability, critical for manufacturing use where ‘free-form’ AI can otherwise skip important constraints.”

• Better validation and standardization, “especially where AI results need to stand alongside or reduce traditional inspection,” he adds.

Challenges to Overcome

To date, there are at least a few obstacles surrounding AI use in AM that are worth mentioning—and tackling, according to the experts. 

For HP, Rangarajan says a big challenge is determining the optimal use cases and assessing where there is true value add. “Not every workflow benefits from AI-based automation, so organizations need to evaluate where AI delivers measurable value rather than applying it by default. In an industry just recovering from inflated expectations, it is important that we build confidence in this technology by delivering value as you scale its relevance in digital workflows and physical hardware.”

Rangarajan also acknowledges technical hurdles relevant to AI, such as “early-stage AI-physics modeling, data privacy needs and the cost of collecting and managing datasets large enough to train reliable models, especially models that generalize well. Beyond technology, manufacturers must upskill teams, build trust in new processes and navigate intellectual property questions related to AI-generated designs.”

Wagner of Authentise says the greatest challenge remains a human-oriented one—trust.

“If AI is going to influence engineering or manufacturing decisions, operators need to understand why it is making a recommendation and be able to judge whether that recommendation is valid,” Wagner explains. “That explainability is essential, both for user adoption and for compliance with quality standards and operating practices. Without it, AI may generate interest, but it won’t be trusted enough to deliver real operational value.”

At Materialise, Van der Schueren says while the novelty of AI may pose a challenge, far greater are the issues of reliability and industrialization, and, as Wagner mentioned, that five-letter word again—trust.

“Manufacturers, especially in aerospace/medical, need evidence that AI-supported decisions are as reliable as established inspection methods, and they need standards,” Van der Schueren mentions. He also addresses issues surrounding natural language: “Natural language makes powerful changes easy, but it also increases the risk of asking for something that violates process constraints (e.g., minimum wall thickness). Users still need domain expertise, and the software needs an orchestration layer to provide context and safety.”

Lastly he cites a need for data and model governance at scale. “AI needs to work across different machines, materials, and environments, with controlled deployment (for example, research vs. production models) and traceability.” 

"There were a lot of firsts this year," says Corey Wardrop, Technical Competition committee chair. "The categories were modified. The venue was new, as was the Technical Competition space, and many of the entrants were first-time competitors or even first-time AMUG attendees."

Twelve AMUG DINOs served on the judging panel, selecting Finishing & Post Processing and Advanced Concepts winners.

Joshua Boykin, Ph.D., senior research chemist with REM Surface Engineering, submitted the winning entry in the Finishing & Post Processing category. He also received the Members' Choice award.

Boykin's entry, titled "Breaking the Powder Barrier: Selective Chemical Declogging Enables Truly Free AM Design," presented a production-ready chemical process that selectively removes sintered powder from fully enclosed internal passages in metal powder bed fusion components without degrading thin-wall geometries. 

Ethan Hartmann, solutions engineer at B9Creations, submitted the winning entry for Advanced Concepts. His entry, "Additive-Enabled Miniature Silicone Component Manufacturing via Sacrificial Tooling," demonstrated a workflow for producing true platinum-cure silicone components at the microscale using high-resolution DLP printing and silicone-safe soluble tooling.

In Advanced Concepts, Halil Tekinalp of Oak Ridge National Laboratory took second place with "Multiplexing Extrusion System (MExS): Multi-material AM System for Tailored Hybrid Composites," a large-scale, dual-extrusion multiplexing system that enabled purge-free, in-bead multi-material printing with core–shell and gradient architectures.

In third place, Jason Jones of Hybrid Manufacturing Technologies entered "Seeing Beneath the Surface: Accelerating AM Adoption through In-situ Volumetric Inspection." Jones' entry presented a hybrid manufacturing workflow that integrates additive manufacturing, machining, and in-situ ultrasonic inspection into a single automated system.

Because of a tie, there were two second-place entries in the Finishing & Post Processing category. Aaron Sherman of HellermannTyton was awarded for "Pip-Boy 3000 Mk V—Prop Replica from the Fallout TV Series," a functional prop replica. His entry leveraged advanced post-processing techniques to transform 3D-printed components into a mechanically accurate, metallic-feeling assembly with optical-grade features and integrated electronics.

Joe Olguin of Sandia National Laboratories also earned second place with "Adapting an As-printed LPBF Design for Ultra-Thin Sectioning," demonstrating a process-driven approach to enabling sectioning of LPBF 316L components.

Joshua Boykin and Ethan Hartmann, or designated representatives, will receive complimentary admission to the 2027 AMUG Conference, where they will share details of their projects and the processes used from the main stage.

The Technical Competition judging panel was comprised of 12 AMUG DINOs, an award that recognizes tenure and contributions. Judges were Dan Braley, Carl Dekker, Bob Diaz, Timothy Gornet, Joerg Griessbach, David Leigh, Nicholas Licari, Jason Lopes, Thomas Murphy, Heather Natal, Jeremy Pullin, and Ed Tackett.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

Colibrium Additive will deliver six metal alloy Material Process Combinations (MPCs), which are metal alloy’s physical and mechanical property data; optimize process parameters; consolidate material and process specifications; and establish design allowables for the properties tested. This includes expanding the existing AlSi7Mg and IN718 packages, and adding 17-4PH and 7050-RAM2 to the current portfolio of 316L, CoCr, and Ti64. A dedicated thin-wall fatigue characterization will help validate the performance and fatigue life of thin-wall geometries.

Under the agreement, and to meet NAVAIR required development timelines, Colibrium Additive will also deliver three M Line metal 3D printing systems and one M2 Series 5 printer required to support the MPC development effort, and an AddWorks services package is also included. This package includes licensed material characterization/data curves, manufacturing process instructions, and select specifications to support the additive manufacture of NAVAIR components, as well as a training program for repeatable production of parts. 

The program also includes a training plan for teams in manufacturing, quality, design, and materials, as well as for machine operators.

“Colibrium Additive is proud to extend its support of NAVAIR with proven metal additive technology and deep application expertise,” says Lars Bruns, executive technology leader at Colibrium Additive. “By combining certified hardware with licensed process data and hands-on training, we are helping accelerate the Navy’s ability to produce repeatable, airworthy components at scale and reduce supply chain risk for critical aviation parts.” 

About Colibrium Additive 
Colibrium Additive, a GE Aerospace company, is a provider of 3D printers, powders, and services for industrial scale metal additive manufacturing. 

Sources: Press materials received from the company and additional information gleaned from the company’s website.

Editorial Director Brian Albright discusses the networking opportunities at the Additive Manufacturing Strategies Show, his impressions of the keynotes and the atmosphere at ASSESS, a vendor-independent simulation user conference hosted by NAFEMS (The National Agency for Finite Element Methods and Standards), and the implications of the research data shared by McKinsey and Cambashi.

Associated Editor Stephanie Skernivitz discusses her visits to AMUG, an additive manufacturing show organized by the users themselves, and how the travel issues' impact on show attendance.

Senior Editor Kenneth Wong talks about the news from NVIDIA GTC, where NVIDIA unveiled a new AI-targeted hardware system, and touted its partnerships with simulation software vendors, and the company's interest in the open-source OpenClaw software.

Designed and used in Europe for more than 30 years, ELECTRIX AI combines electrical engineering, control cabinet design, fluid and process engineering, electrical installation, and building automation in one electrical CAD solution. Built-in AI capabilities accelerate engineering workflows, allowing electrical engineers and designers to reduce manual work and automate the creation of finished parts and products, such as design schematics, supporting files, and bill of materials.  

ELECTRIX AI helps users work across tasks, from importing schematic data, exchanging components, and validating project requirements and compliance to translating documentation and automatically generating control cabinets, wiring and finished design files. 

“Electrical engineers do not need more hype around AI, they need practical tools that help them get real work done faster,” says Dr. Axel Zein, CEO of WSCAD. “ELECTRIX AI gives engineers an AI-powered assistant that supports their work inside the CAD environment, helping them save time, work smarter, reduce friction in the design process and create capacity for more innovation.” 

ELECTRIX AI is fully equipped to meet U.S. electrical specifications and regulatory requirements. It comes with complete support for NFPA 79, includes all the relevant IEEE 315 / ANSI Y32.2 symbols, and lets users design projects in full compliance with NFPA 70 (NEC) and UL 508A. 

Key Product Features  

ELECTRIX AI streamlines steps in the electrical design process. Key capabilities include:  

• AI-assisted project checks: Users can upload project requirements and have the software review schematics against those specifications.
• Automated project translation: Engineers can translate electrical project documentation into more than 102 languages directly inside the software. 
• Control cabinet generation: Once schematics are complete, the software can automatically generate layouts, place components and support routing workflows. 
• Library of components and schematics from U.S. manufacturers such as Rockwell Automation, Eaton, Honeywell, Phoenix Contact, nVent Hoffmann, TE Connectivity and more. In all, more than 2.2 million files.  

To learn more of the platform’s capabilities, an interactive webinar is slated for May 6, 2026 at 1 p.m. CST. Register at this link.   

About WSCAD GmbH 

WSCAD is a provider of AI-powered electrical CAD software. Reportedly, more than 40,000 users across 100+ countries turn to WSCAD for machine and plant engineering as well as installation technology. Apps such as Cabinet AR and Building AR support digital designing and service processes. The electrical CAD data library wscaduniverse.com provides over 2.2 million item datasets free of charge. 

Sources: Press materials received from the company and additional information gleaned from the company’s website.

The companies’ work will help reduce iterations and improve correlation for design technology co-optimization-focused advanced AI and high-performance computing designs. 

“AI silicon innovation at advanced nodes demands a signoff-ready approach that spans the full design cycle and scales from SoCs to chiplet and 3D-IC architectures,” says Chin-Chi Teng, senior vice president and general manager, Cadence. “Through collaboration with TSMC, we’re advancing our Design for AI and AI for Design strategy by uniting certified flows with silicon-proven IP and building the agent-ready foundation that will help engineers improve productivity as complexity continues to rise.”

"The growing demands of AI compute workloads, combined with compressed design cycles, require advanced, energy-efficient silicon technologies, streamlined design flows, and silicon-validated IPs," says Aveek Sarkar, director of the Ecosystem and Alliance Management Division at TSMC. "Through our collaboration with Open Innovation Platform (OIP) ecosystem partners like Cadence, we empower customers to confidently design cutting-edge silicon using TSMC’s latest process technologies and 3DFabric® advanced packaging solutions—unlocking transformative opportunities for AI-driven innovation."

Design for AI

Cadence is delivering an IP portfolio for TSMC N2P, including DDR5 12.8G MRDIMM, PCIe 6.0, LPDDR6/5X 14.4G and HBM4E 16G. The Cadence Artisan foundation IP advanced-node portfolio is now in production designs using TSMC N3 process technologies.

Cadence enables semiconductor teams with certified, end-to-end EDA flows that scale from advanced-node SoCs to chiplet and 3D-IC designs, including implementation with the Innovus Implementation System; custom/analog implementation and simulation with Virtuoso Studio and the Spectre Simulation Platform; thermal analysis with the Celsius Thermal Solver, Voltus IC Power Integrity Solution, and EMX Planar 3D Solver; and signoff technologies with Tempus Timing and ECO Solution, Quantus Extraction Solution, Liberate Characterization Portfolio, and Pegasus Verification System; all certified for TSMC N2 and A16, and ongoing collaboration for A14 PDKs.​​ Additionally, the Genus Synthesis Solution is enabled for these process technologies and ongoing collaboration on Clarity 3D Solver.

For 3D-IC and heterogeneous integration, the Cadence Integrity 3D-IC Platform supports the TSMC-COUPE Reference Flow for stacked-die, while Virtuoso Studio’s heterogeneous integration methodology adds silicon photonics support. Celsius thermal-aware flow is enabled including PIC placement with Virtuoso and signal integrity analysis with EMX. 

AI for Design

Cadence’s agentic AI shifts EDA from tool-by-tool workflows to goal-driven, agentic execution. Working with TSMC, Cadence is preparing “agent-ready” design flows, optimization engines, and signoff infrastructure.

The enhanced Genus Synthesis Solution, Innovus Implementation System, and Cadence Cerebrus Intelligent Chip Explorer’s AI-driven implementation is optimized to support TSMC NanoFlex Pro standard cell architecture for DTCO. In addition, front-end placement and back-end routing rules improve correlation between pre-route and post-route results; and TSMC’s A16 Super Power Rail enables denser and faster designs by routing power nets on the backside of the chip.​

In custom design, Cadence has embedded agentic AI in Virtuoso Studio flows with circuit optimization for TSMC process technologies. This includes the enablement for N2-to-A14 Analog Design Migration flow.​

Sources: Press materials received from the company and additional information gleaned from the company’s website.

The Eigen Engineering Agent operates within real engineering systems to plan, execute, and validate tasks. It understands its projects, writes automation code, configures systems, and iterates until predefined performance benchmarks are met.

“This is a defining moment for industrial AI—where the technology becomes as easy to use as consumer AI, yet far more consequential,” says Peter Koerte, member of the Managing Board of Siemens AG and the company’s chief technology officer and chief strategy officer. “The Eigen Engineering Agent creates concrete business value for our customers. And it has the potential to fundamentally transform how they design, build, and operate the industrial systems we rely on.” 

In pilot deployments with more than 100 companies in 19 countries, the Eigen Engineering Agent accelerated common engineering tasks such as programmable logic controller (PLC) coding, human-machine interface (HMI) visualization, and device configuration.

The Eigen Engineering Agent is production-ready and available to the more than 600,000 users of Siemens’ Totally Integrated Automation Engineering platform, TIA Portal. It is part of the Siemens Xcelerator portfolio and is digitally available now. 

This product launch is part of Siemens’ €1 billion investment in industrial AI, announced in November 2025.

To date, Siemens has more than 1,500 AI experts and holds more than 2,000 AI patent families worldwide. With plans to create an industrial AI operating system for the physical world, the company is embedding AI across its portfolio and creating new AI-native products, such as the Eigen Engineering Agent. 

Sources: Press materials received from the company and additional information gleaned from the company’s website.

What the Server Enables

The Product Help MCP Server provides access to Autodesk Help content across 110+ products. Whether getting started with a product, exploring new capabilities, or resolving an issue, AI assistants can now reference authoritative Autodesk documentation.

The Autodesk Product Help MCP Server is available now for free. As gold-level members of the Agentic AI Foundation, Autodesk says it is collaborating with the industry to advance open protocols, tooling, and best practices for agent-based AI systems.  

Autodesk plans to continue exploring ways to expand MCP support, within Autodesk Assistant and through additional MCP-based offerings.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

Highlights of the Release

New Machining Operations

• Automatic 3+2 Roughing Setups with adaptive roughing support.
• 4-Axis Extrusion Machining with Across, Along, and Helical patterns.
• 4-Axis Extrusion Projection Machining for projecting curves onto 3D surfaces.
• 2.5-Axis Automatic Deburring with Ball, Vee, Chamfer, & Taper Mills.

GPU-Accelerated Tri-Dexel Simulation

• Triple-orthogonal dexel model enabling fast, accurate simulation.
• Significant speedups across 2.5, 3, 4, & 5-axis operations using the GPU.
• Superior performance handling high toolpath complexity.

Fixture & Toolpath Enhancements

• Dedicated 3D fixtures for each setup with multiple-fixture support.
• Arc-based feed rate optimization, including slowdown in corners.
• Improved drill point sorting using last point continuation.

Post-Processor & API Updates

• Support for drill heads, dual-head machines, and non–G-code formats.
• New G68.2 rotated-plane machining capability.
• Expanded API for setups, operations, workflows, and simulations.

Stability & Performance Improvements

• Enhanced reliability across operations.

Free demo software downloads of RhinoCAM 2026 and VisualCAD/CAM 2026 can be requested here.

About MecSoft Corporation

Headquartered in Dana Point, CA, MecSoft Corporation is a worldwide leader in providing Computer Aided Manufacturing (CAM) software solutions for the small to mid-market segments. These solutions include the VisualCAD/CAM®, and RhinoCAM™ products. These software products deliver powerful, easy-to-use and affordable solutions for users in the custom manufacturing, rapid prototyping, rapid tooling, mold making, aerospace, automotive, tool & die, woodworking, and education industries.

“At shop level, the pressure is always the same: get the job right and get it moving,” says Zoltan Tomoga, product manager at Hexagon’s Production Software Division. “The latest EDGECAM release focuses on removing delays in everyday programming work, whether that means reviewing a long simulation instantly, helping operators understand the machining process more clearly, or making it easier for programmers to find the right capability inside the software.”

EDGECAM now stores snapshots at each tool change during simulation, allowing users to jump backwards through a machining sequence more quickly, the company says. In complex jobs, rewind times can drop from around 30 seconds to close to 1 second reportedly, helping programmers review long programs without interrupting the workflow.

The release also enhances communication between programming and production teams. A full 3D machine simulation can now be recorded and embedded directly into Live Job Reports, allowing operators to review machining processes interactively in a browser, even without an EDGECAM license. They can zoom, rotate, adjust playback speed, and scrub through the timeline to better understand how the job will run before pressing cycle start.

Alongside these verification improvements, EDGECAM features enhancements across several toolpath cycles. Updates include support for lens tools in the Parallel Lace cycle, smoother roll-in movements in Face Milling, improved holder-aware collision avoidance in Rest Roughing, and expanded Race Line controls for more selective finishing operations. 

The release also introduces Hexagon Copilot for EDGECAM, bringing natural-language assistance into the CAM environment. Programmers can ask questions, locate functions, and access guidance more easily inside the workflow.

“Manufacturers are interested in AI, but they’re also very practical,” Tomoga adds. “For most shops, the value comes when technology helps them work faster without changing how they already operate. Copilot is designed to help programmers get more out of EDGECAM by making the system easier to navigate and easier to learn.”

EDGECAM already supports advanced automation through feature recognition, Strategy Manager, and knowledge-based machining workflows. Copilot builds on this foundation by helping users access those capabilities more efficiently, the company says.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

According to Mordor Intelligence, cloud and SaaS simulation software is expected to have a 13.22% compound annual growth rate (CAGR) through 2031, although on-premises installations still hold the majority of the market right now (60% of revenue in 2025). The report notes that Siemens added 1,200 new cloud customers in 2025, for example. Hybrid scenarios are also common, with some workloads running on local high-performance computing (HPC) infrastructure, and others in the cloud.

Another research firm, Strategic Revenue Insights, put the cloud-based CAD market at $12 billion by 2033 with a CAGR of 8.5%. ABI Research also reported that cloud-based CAD is growing at roughly twice the pace of traditional CAD, and could account for roughly 33% of the market by 2035.

To get a better idea of the current state of adoption and some of the drivers of that growth, we spoke to executives from three firms that have been leaders in the shift to cloud—Autodesk, Synopsys (which recently acquired Ansys), and PTC, the parent company of Onshape.

DE: How has adoption of your cloud-based tools changed over the past few years—steady adoption, accelerated adoption, etc.? What trends are you seeing? 

Jon den Hartog, vice president of Design, Autodesk: We’ve seen an accelerating increase in design and manufacturing teams with cloud-based tools over the past few years. As teams become more distributed, products become more complex, and consumer demands increase, manufacturers are recognizing the value of connecting the entire design-and-make process—from end to end. 

Wim Slagter, Senior Director, Partner Programs, Synopsys: I would say that adoption has accelerated over the past few years, rather than just growing steadily. Earlier on, most companies were experimenting with cloud for pilots or for burst compute during peak workloads. Now we are seeing more organizations running production workloads in the cloud.

With electronic design automation (EDA) specifically, there is strong momentum because there are more compute-intensive workflows as chip design gets more complex. There is more AI-driven design exploration requiring massive compute capacity, and the cloud allows companies to scale it quickly without having to build out huge on-premise clusters. On the CAE [computer-aided engineering] side, adoption has also grown, but tends to be more granular. Simulation teams often have long workflows tied to local HPC infrastructure, so the transition has been more of a progressive shift toward cloud-enabled HPC rather than a full move to SaaS.

Darren Henry, Senior Vice President of Operations for Onshape at PTC and Marketing for Arena by PTC: Adoption of cloud-native CAD has accelerated dramatically over the past several years. In the early days, most of the demand came from startups and smaller hardware companies that wanted to avoid the cost and complexity of traditional CAD and PDM infrastructure. Today we’re seeing strong momentum from mid-size and enterprise organizations that are actively modernizing their engineering environments.

OnShape is a cloud-native design tool. Image courtesy of PTC.

The reality is that most traditional CAD systems were architected decades ago around files, local workstations, and separate data management systems. That model no longer reflects how engineering teams actually work. 

Another major trend is the growing appetite for AI-driven engineering workflows. Companies increasingly want tools that help engineers work faster by automating repetitive tasks, surfacing insights, and enabling better decisions throughout the design process. Cloud-native platforms provide the kind of unified environment that makes those capabilities possible.

DE: What are some of the common drivers for adopting cloud-based design/engineering tools? 

den Hartog, Autodesk: The biggest drivers are collaboration, data management, and the need for greater speed and agility with access. Cloud platforms allow teams to work from a shared, centralized dataset with built-in version control, wherever they are, reducing errors while enabling cross-functional workflows across design, simulation, and manufacturing. These capabilities help lean teams move faster and stay competitive, and we’re seeing similar patterns in other industries where connecting teams and data is becoming critical. 

Slagter, Synopsys: The biggest one is access to scalable compute resources. Instead of maintaining large clusters that are idle much of the time, you can scale up and down in the cloud as needed. The other major driver is time-to-market pressure. With access to large numbers of cores and GPUs [graphics processing units], engineers can run more simulations or verification jobs in parallel, which speeds up design cycles.

Cost and infrastructure management also plays a role, since on-prem requires significant capital investment and active management, where the cloud allows companies to shift to more flexible consumption models.

Collaboration is possible thanks to the cloud, too. Many engineering teams are globally distributed, and cloud environments make it easier to standardize tool environments and share data to geographically distributed locations.

Henry, PTC: A major driver is simple frustration with the limitations of traditional CAD environments. Engineers still deal with crashes, lost work, complex file management, and collaboration workflows where only one person can safely edit a design at a time.

At the same time, engineers see how efficiently modern SaaS applications operate across the rest of the business. Cloud software is already the norm in areas like sales, finance, and operations. As a result, there is growing expectation that engineering tools should offer the same level of reliability, accessibility, and collaboration.

Ultimately, companies are looking for productivity gains that have largely plateaued with file-based tools. Cloud-native platforms remove many of those bottlenecks by enabling real-time collaboration, eliminating version conflicts, reducing IT overhead, and simplifying how teams manage design data.

DE: What are the key challenges? 

den Hartog, Autodesk: Historically, there have been two challenges. The first was data security and IP protection. Now, cloud platforms routinely offer security capabilities that meet or exceed what most organizations can achieve on premise. The second was the difficulty of moving from established formats and systems to modern cloud environments. This, too, is becoming less of a barrier to entry as connectivity improves and data interoperability matures, making it easier to bridge the gap between existing workflows and cloud-native platforms. 

Autodesk is adding AI-based Neural CAD capabilities to its cloud-based Autodesk Fusion offering. Image courtesy of Autodesk.

Slagter, Synopsys: When I talk with large enterprise customers, one of the biggest concerns is cost predictability. The cloud is great for burst capacity, but for continuous workloads they still worry about spend control and license consumption. We explicitly recommend hybrid for burst, while also stressing cost transparency and long-term sustainability. 

Data security and IP protection are also important concerns. Some engineering models can be extremely sensitive, and companies want guarantees over where data resides. Data movement and integration with existing workflows are also a concern, because EDA and CAE workflows involve large data sets. But the shift has really been more to worry about cost predictability.

Henry, PTC: The biggest challenge is usually not the technology—it’s the mindset shift.

Engineering teams have spent decades working with file-based CAD systems, and naturally they evaluate tools based on the workflows they use every day: sketch, extrude, fillet, shell. What is often harder to see is the broader impact of the underlying infrastructure: things like file management overhead, version conflicts, IT maintenance, and deployment complexity.

An individual engineer may feel productive in their daily tasks, but across the organization a surprising amount of time can be lost managing files, coordinating changes, and maintaining PDM systems.

Legacy data is sometimes another consideration, since many companies have years of existing CAD files that need to be incorporated into a new environment. However, once teams begin working in a truly cloud-native platform and experience capabilities like real-time collaboration and built-in data management, adoption tends to accelerate quickly.

DE: Are mixed/hybrid environments common? Do your users tend to want to shift to an entirely cloud-based environment, or prefer hybrid? 

den Hartog, Autodesk: Hybrid environments are the norm today, with many companies continuing to use desktop tools alongside cloud-based platforms for collaboration, data management, and connected workflows as they transition. Cloud platforms are evolving quickly, and with that evolution comes a fundamental shift in how automation can be applied across the full arc of product development and manufacturing. We expect customers to gravitate toward platforms that deliver the majority of capabilities in the cloud, like AI, complemented by edge devices that ensure performance, responsiveness, and a great user experience where they need it. 

Slagter, Synopsys: I would say hybrid arrangements are most common, and the dominant model today in our space. Many companies continue to run a portion of workloads on premises while using cloud for burst capacity, and some workloads that benefit from large-scale compute. Most enterprises don’t want to move everything to the cloud immediately; they want flexibility to combine on-prem with cloud resources. Fully cloud-based environments tend to be more common among startups or smaller teams that want to avoid building their own infrastructure.

Henry, PTC: Hybrid approaches do exist in the market. Some CAD systems store files in the cloud but still rely on local desktop applications for computation. While that can provide some flexibility, it still leaves teams operating in a split environment between desktops and cloud infrastructure and retains many of the same file-based workflows that slow collaboration.

Fully cloud-native platforms like Onshape take a different approach by operating entirely in the browser. That means no installations, no version conflicts, and immediate access from anywhere. More importantly, design data, compute, and collaboration all exist in one unified environment.

It also creates a structural advantage for AI. When design data, compute, and user activity all exist in a single cloud platform, multiple AI tools can connect directly to the same environment and work alongside engineers in real time. There’s no need to move files between systems or manage copies of data. AI agents can interact with the same live models engineers are using. In hybrid environments where data is fragmented across desktops and cloud services, delivering that level of seamless collaboration becomes far more difficult.

DE: What technological developments are having the most impact on the capabilities of these cloud-based tools? 

den Hartog, Autodesk: Without question, it’s AI and automation. Autodesk is focused on AI that directly improves the way designers and engineers work, accelerating development and amplifying their innovation capacity. We’re seeing our small and mid-sized customers use AI with measurable results, especially with generative design in product development. For example: one customer achieved a 40% reduction in the weight of a critical EV [electric vehicle] component, while another automatically generated an optimized lightweight drone to cut mass in half while meeting performance goals.   

Slagter, Synopsys: AI is becoming a major accelerator, and is increasingly being used for tasks such as design space exploration, optimization, predictive modeling, etc. These AI approaches require large amounts of data and compute power, and naturally fit with cloud infrastructure.

Henry, PTC: Artificial intelligence is beginning to play a meaningful role in engineering software. We are starting to see AI assist engineers by automating repetitive tasks, analyzing design data, and surfacing insights that might otherwise take significant time to uncover. Advisor-style tools can help guide engineers through workflows, flag potential issues earlier, and recommend improvements during the design process.

Those capabilities are still evolving, but the direction is clear. We will see AI assistants become increasingly capable and eventually evolve into agents that can automate portions of the engineering workflow.

Another important development is the rapid growth of robotics and advanced simulation environments. Platforms such as NVIDIA Isaac Sim and Google’s MuJoCo are becoming essential tools for developing and testing robotic systems and other forms of physical AI.

As robotics and simulation mature, the ability to connect design systems directly to those environments becomes increasingly important. When CAD and simulation platforms are tightly connected, teams can iterate faster and validate designs much earlier in the development cycle.

DE: How do you see the market changing/evolving over the next five years? 

den Hartog, Autodesk: Over the next five years, AI will reshape design and manufacturing in ways we’re only beginning to see. Today, customers are adopting task-level AI because it meets them where they are. Take our AI-powered Sketch AutoConstrain in Fusion. Since its launch last year, the AI model has delivered over 3.8 million constraints, up from 2.6 million last quarter. The signal is clear. 

Next, workflow automation will move customers faster through product development, broadening the alternatives they can explore, and bringing manufacturability into the design process from the start. We’re seeing this take shape in agents that handle multi-step workflows within and across products. 

The next frontier is systems automation. Here, the platform carries contextual awareness of work patterns, engineering requirements, manufacturing options, and performance. Agents at this level surface opportunities for innovation, efficiency, error avoidance, and faster time to market. Employees will guide these agents, take on more projects, compete more effectively, and expand their business. 

Slagter, Synopsys: Cloud infrastructure will become a standard part of engineering.

Rather than replacing on-prem entirely, the industry will continue moving toward hybrid environments. Another major trend will be the growing role of AI-driven design and simulation. As an ISV [independent software vendor], Synopsys is already pushing forward with multi-agent AI automation and AI-driven multiphysics simulation. As a result, compute demands will grow for sure, making elastic cloud increasingly essential. I would expect much tighter integration across the engineering stack. Our new Multiphysics Fusion technology and the first unified Synopsys-Ansys workflows point to a market where chip design, system level simulation, and multiphysics modeling come together in a single environment.

Henry, PTC: Over the next five years, cloud-native product development platforms will increasingly become the standard for engineering teams.

File-based CAD systems were built for a different era of product development. As engineering teams become more distributed and products become more complex, the limitations of those architectures become more apparent.

Engineering workflows will continue to move toward more collaborative, connected environments that span the entire product lifecycle.

At the same time, AI will play a much larger role in how products are designed. Today AI primarily assists engineers through recommendations. In the coming months and years we will see more autonomous capabilities that help automate portions of the design process and dramatically accelerate iteration. 

Recent new launches for the platform include FDM (fused deposition modeling) infill density optimization and an instant lattice engine.

With predictive gyroid infill optimization for FDM builds, users can easily set a target performance goal, and Vixiv AI calculates the exact infill needed, eliminating any overbuilding. The optimization time is less than 30 seconds.

A model is trained on the Prusa CORE One+ printer using Atomic Filament PETG Pro.

Benefits to Engineers who Prototype

• Less filament waste
• Fewer failed prints
• Applicable results on first try

Instant Lattice Engine

With the instant lattice engine, users can design and export complex lattices immediately. You can design, customize, and visualize lattices with ease, without advanced setup.

The update offers a faster path from concept to functional geometry, and the freedom to create lattice structures that were previously difficult or time-consuming to achieve, the company reports.

Learn more about this release at www.vixiv.net

About Vixiv

Vixiv, based in Cincinnati, OH, uses AI-enabled predictive software to reduce material usage and speed up design iteration.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

The joint effort by EMA and Synopsys focuses on reducing risks to extravehicular activity (EVA) systems, specifically spacesuits, caused by triboelectrification from lunar regolith interactions, and electrical charging and electrostatic discharge (ESD) from the space plasma environment. Analyzing charging levels that the complex, multi-layer Artemis spacesuits may experience on the moon is important for sustained lunar surface operations, because ESD events can damage mission-critical electronics needed for communications and life support, according to Synopsys.

Under the planned approach, EMA and Synopsys will apply and develop physics-based analysis workflows using Ansys Charge Plus, a software simulation tool for electromagnetic charging and discharging, to evaluate spacesuit materials, layered stack-ups, and representative suit features across relevant lunar plasma conditions. These simulation efforts are paired with test and validation activities conducted at EMA's Space Environment and Radiation Effects (SERE) Laboratory in Pittsfield, MA, a facility capable of replicating key aspects of the space plasma environment on the ground. 

"We're honored to support NASA's Johnson Space Center as they advance EVA readiness for Artemis," says Justin McKennon, chief technology officer of EMA. "By pairing test-informed data with simulation workflows, we can help identify worst-case charging conditions, evaluate material stack-ups, and target validation where it matters most."

In addition to spacesuit validation, Cesium integrated 3D spatial and true-to-reality Moon topography data into Synopsys' digital mission engineering environment, where radio frequency (RF) signal propagation performance is analyzed using Ansys RF Channel Modeler software. Ansys HFSS simulation software is also included in the technology stack for high-fidelity antenna models installed on spacesuits and rovers.

"To build a lunar network, you must first build a digital moon," says Patrick Cozzi, chief platform officer, Bentley Systems. "Cesium's high-fidelity digital twin provides a virtual stage to test how communication signals perform against complex lunar topography, validating network reliability and ensuring mission-critical connectivity before hardware is deployed."

The Lunar 3GPP team at NASA's Glenn Research Center leverages this solution to visualize and validate RF coverage in the context of realistic operating scenarios. The insights can help inform radio placement that will enable connectivity outside of a future Moon Base. 

"The Artemis program is an ambitious, collective effort to return humans to the Moon and establish a sustained presence as a foundation for future exploration," says Jim Bridenstine, former NASA Administrator and current advisor for AGI, part of Synopsys. "Embracing digital engineering technologies that enable teams to model, test, and refine designs virtually before hardware is built, is an important step to reducing risk and accelerating innovation."

Sources: Press materials received from the company and additional information gleaned from the company’s website.

AMUG representatives presented the DINO Awards, honoring tenure in the AM industry, years of service, industry contributions, and active support of AMUG and its conference.

"DINO Committee members are DINO recipients committed to recognizing other AMUG members who have made significant contributions to the organization and the industry," says Bruce LeMaster, DINO Selection Committee chair and AMUG's director at large. "We evaluate each nominee based on their tenure in the industry, commitment to promoting additive manufacturing, and support for AMUG. Those who are ultimately selected for a DINO Award exemplify a 'giving' attitude that complements their knowledge of additive manufacturing."

This year's AMUG DINOs are:

• Olga (Dr. O) Ivanova, Mechnano
• Daniel Landgraf, 3D Spark
• Brian Post, Oak Ridge National Laboratory
• Chris Prue, IperionX
• John Thiell, Beehive Industries
• Annie Wang, Senvol

The selection committee cited the key activities for which the 2026 DINO Awards were presented.

"This year's DINO recipients come from a broad range of industries, government labs, equipment and material OEMs, and additive manufacturing service providers," says LeMaster. 

Olga Ivanova, Ph.D., has led an active career in AM since her time at the University of Louisville and Virginia Tech. Jason Lopes, DINO Selection Committee Member, notes, "If you've ever been to an AMUG Conference, you've probably met Dr. O and been drawn into deep discussions spanning all things additive." 

Carl Dekker, DINO Selection Committee member, refers to Danial Landgraf as the consummate professional. "Daniel has served on the AMUG Board and the International Committee. He's never met a stranger and is always happy to talk about additive manufacturing," Dekker says.

According to Alex Roschli, AMUG director of Education & Conference, "Brian Post is a huge proponent of additive manufacturing and the AMUG organization. He is always willing to help others with additive projects, answering their questions or referring them to other SMEs [subject matter experts]."

Shannon VanDeren, AMUG President, said that Chris Prue is "the guy who is always willing to help!" He has been volunteering at AMUG Conferences for years.

John Thiell was president of AMUG in 1996 (then known as 3DSNASUG) and served on the board for four years. Because of a shift in work responsibilities, he was unable to attend AMUG Conferences for years, but upon his return, he served on several AMUG committees.

"Annie Wang is one of those people that you want on your team," according to Kevin Ayers, 2004 DINO Award Recipient. "She always has a smile and is always willing to lend a hand." Annie volunteers at AMUG Conferences and helps connect AMUG members.

Over its 38-year history, AMUG has awarded 205 DINOs. LeMaster noted that 59 DINOs were present at AMUG 2026, representing 29% of all that have received this recognition.

Nominations for 2027 DINO candidates will be accepted beginning October 1, 2026.

ABOUT ADDITIVE MANUFACTURING USERS GROUP (AMUG)

The Additive Manufacturing Users Group (AMUG), a 501(c)(6) nonprofit corporation, is committed to encouraging and coordinating exchange of technical information among those who use additive manufacturing. 

Sources: Press materials received from the company and additional information gleaned from the company’s website.

The Cadence ChipStack AI Super Agent integrates advanced agentic reasoning with Cadence’s electronic design automation solutions, delivering  productivity improvements across digital design and testbench development, verification planning, regression management and automated debug. By combining AI agents with native EDA execution, the platform enables design teams to compress development cycles, improve efficiency and accelerate time to tapeout.

“Our collaboration with Google Cloud represents a major step forward in scaling AI-driven design automation,” says Paul Cunningham, senior vice president and general manager at Cadence. “By integrating the Cadence ChipStack AI Super Agent with Gemini, we’re advancing the next generation of agentic design—combining the reasoning power of large language models with Cadence’s world-class EDA engines to deliver breakthrough productivity and quality of results for our customers.”

At the core of the ChipStack AI Super Agent is its Mental Model technology, which enables agentic reasoning through Cadence native skills, driving Cadence EDA tools to improve the quality and correctness of large language model (LLM)-generated content. Through its collaboration with Google Cloud, Cadence is tightening the integration between the Gemini-enabled ChipStack AI Super Agent and Cadence EDA engines.

This collaboration also leverages Google Cloud’s secure, elastic compute infrastructure to deliver the compute needed for Gemini’s LLM reasoning, Cadence EDA tools and the ChipStack AI Super Agent. The ChipStack AI Super Agent is available now on the Google Cloud Marketplace.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

ENCY calculates the machining result and runs checks during toolpath calculation. In ENCY 2.8 and ENCY Robot 2.8, background simulation during toolpath calculation can now be configured more flexibly. System settings now let you define separately both the machining result calculation and the level of checks performed at the toolpath calculation stage.

If background machining simulation is disabled, the system calculates only the toolpath from the 3D model, while the machining result can be checked later in Simulation mode.

At the same time, operations that require the result of the previous machining step for correct calculation continue to work properly: ENCY automatically calculates the required intermediate result for Waterline Roughing, Plane Roughing, Pocketing, Hole Machining, and all lathe machining operations. For example, when calculating Waterline Roughing, the result of Face Milling may also be calculated, while 3D Helical does not require the result of the previous operation.

If background machining simulation is enabled, the machining result is available immediately after toolpath calculation. This makes it easier to evaluate the state of the part or workpiece without launching Simulation mode separately, which is especially useful in projects without a large number of fine-step finishing operations. At the same time, collision detection may be disabled.

The level of collision detection can also be selected separately: holder-only collision detection for faster calculation, the holder + machine scenario, or full control of all machine nodes.

Depending on the selected settings, the new operation statuses immediately show how thoroughly the calculated toolpath has been verified. To support this, we introduced different operation statuses.

ENCY 2.8 adds bar pulling during Part-off with takeover. It is now possible to control both the pulling length and the clamping depth. The operation’s CLData can define a strict sequence of motions in the NC program:

• turret indexing
• approach to the plunge position along the Z axis
• counter spindle approach
• picking and pulling the workpiece
• approach to the plunge position along the X axis
• part-off
• retraction of the tool and the counter spindle

For more product details, click here

Sources: Press materials received from the company and additional information gleaned from the company’s website.

The total market grew 10.2% in 2025 compared to 8.3% in 2024. Metals totaled $6.27 billion, while polymers reached $9.79 billion in 2025. The combined AM services market was valued at $8.53 billion.

Growth in metal AM in 2025 came from the medical, space, and defense/maritime sectors. The AM print services market, which will be the largest segment over the next decade, is undergoing continual evolution, the report says. According to the company: “The integrated provider approach is seeing a resurgence with companies such as Fabric8Labs, Seurat, and even Velo3D all capitalizing on an environment that highly values AM but often struggles to leverage it effectively.”

Image courtesy of AM Research.

Metal additive print services overtook polymer services worldwide for the first time in 2025, driven by contract manufacturers and integrated production service models, in which printer manufacturers offer printing services as well as hardware.

"2025 was a story of two markets, with the first half of the year largely in line with the tougher times of the last two years, but in the second half of 2025 we saw what we believe is the start of a significant turnaround for AM,” said AM Research executive vice president Scott Dunham. “The industry is still carrying too much weight competitively and a number of companies exited in 2025 – this won't stop in 2026. But it's become clear we're at a point where growth is flowing back into the market."

Image courtesy of AM Research

On the polymers side, the consumer goods market is printing tens of millions of parts using low-cost extrusion methods, and the announcement that Lego is now 3D printing could have a huge impact. “The implications of Lego even doing some of its output in 3D printing instead of injection molding has big implications for the industry,” Dunham said in a briefing.

Materials saw softer sales in 2025 because of slower hardware sales, Dunham said, but there will likely be a more than doubling in metal AM powder shipments by 2028. Hardware shipments are also expected to grow moving forward.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

Used in manufacturing for noncontact inspection, reverse engineering, and CAD comparison, Laser Line Probes are often used on dark, shiny, reflective, or contrasting surfaces that may require additional setup, multiple passes, or manual adjustment. 

Powered by DTEX, FARO CREAFORM’s approved LLP processing pipeline, the update focuses on providing results for materials while also reducing the need to fine-tune acquisition parameters. Beyond LLP improvements, the update also brings attention to the FARO 8‑Axis Max, a metrology‑grade rotary worktable designed to extend the reach of FaroArm‑based measurement systems. When used with the Quantum X FaroArm and LLPs powered by DTEX, the 8‑axis Max acts as a force multiplier for scanning larger or more complex parts.

By measuring part rotation through integrated encoders and combining with synchronized data from the arm, it allows DTEX-enhanced data to be used across the entire part in a single smooth coordinate measurement workflow.

The update applies across the Laser Line Probe portfolio, extending the practical value of these systems in everyday measurement scenarios.

• FAROBlu xR: Offering high-resolution scans, providing results for small features.
• FAROBlu xS: Offering scan times, enhancing throughput in demanding manufacturing applications.
• FAROBlu xP: A balance of resolution, maintaining throughput while providing dense and accurate data
• Quantum X FaroArm Series: Improved LLP scan accuracy across a wider range of part finishes, supporting inspection, reverse engineering, and quality control workflows.
• FARO 8‑Axis Max rotary worktable: When combined with a Quantum X arm and FAROBlu LLP, provides an efficient workflow by reducing device moves and improving ergonomics.

About FARO CREAFORM

With headquarters in Lévis, Québec, Canada and Lake Mary, FL, FARO CREAFORM is the result of the combination of FARO’s 3D Measurement business segment and Creaform’s metrology solutions. 

Sources: Press materials received from the company and additional information gleaned from the company’s website.

This agreement builds on TECHNIA's long-standing Global Platinum Partnership with Dassault Systèmes and reflects a shared commitment to providing engineering teams with digital continuity, from conceptual design through to manufactured products. By adding Euro-Central to the distribution territory, TECHNIA will be able to serve customers across a broader European footprint with locally grounded expertise.

“I’m delighted that we are expanding and continuing to strengthen our partnership with Dassault Systèmes, bringing the SOLIDWORKS portfolio to a broader European market," says Magnus Falkman, CEO, TECHNIA. 

“With almost 40 years of CAD/CAM and PLM experience and vast expertise across the Dassault Systèmes portfolio, we’re uniquely positioned to serve the SOLIDWORKS community," says Jens Pottoff, managing director, TECHNIA Euro-Central. 

TECHNIA is a multinational provider of digital engineering software, services, and consulting. TECHNIA has 600+ employees across 30+ office locations.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

This release introduces a multiphysics workflow, alongside improvements in usability, meshing and post-processing performance, focused on simplifying the setup and analysis of complex automotive simulations.

Updated versions of the ELEMENTS-Adjoint and ELEMENTS-Coupled modules are also included, aligned with the new capabilities available in ELEMENTS 4.5.0.

What’s New in ELEMENTS 4.5.0?

Version 4.5.0 introduces several key additions and enhancements, including:

Improved Simulation Workflow

• New Multiphysics Interface: define different physics and solver settings on different mesh regions, enabling flexible modelling of complex vehicle scenarios.

• Modular Modeling Environment: refactored setup interface with clearer structure and improved navigation.

Enhanced Meshing and Performance

• Faster Meshing Performance: improvement in meshing speed with reduced memory usage.

• Improved Mesh Control: better handling of boundary layers, feature-based refinement, and support for reference frames in mesh generation.

• Flexible Mesh Operations: combined surface and volumetric refinement, and improved mesh splitting workflows.

Faster Post-Processing and Analysis

• New Native Case Reader: faster post-processing with reduced memory usage.

• Advanced Visualisation Tools: multislice and multi-isosurface generation, streamline surface visualization, and direct export to PVD format.

Improved Usability Across the GUI

• New view cube for intuitive navigation.
• Interactive plane manipulation for faster analysis.
• Surface selection and highlighting tools for easier setup.
• Improved management of probes and material points.

Expanded Modeling Capabilities

• Solar radiation with time-dependent inputs and local transmissivity.
• Improved handling of partially overlapping non-conformal interfaces (NCC).
• Additional field processing tools for pressure analysis.

Access ELEMENTS 4.5.0 

Existing customers can download ELEMENTS 4.5.0 from the ENGYS Customer Portal. For new users, contact us to learn how ELEMENTS can support automotive CFD workflows.

Learn More About ELEMENTS 4.5.0

Watch this video to learn more about this update.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

OpenUSD operates as a foundational technology for composing and exchanging complex 3D scenes and environments. According to Aras, It underpins modern industrial digital twin platforms—including NVIDIA Omniverse, whose libraries and APIs leverage OpenUSD to enable real-time collaboration, simulation, and physical AI applications across engineering, manufacturing, and operations.

Aras’ participation in AOUSD reflects its long-standing commitment to open ecosystems and its focus on bridging PLM-governed digital thread data with scalable OpenUSD-based 3D environments used across engineering, manufacturing, quality, and service.

“Open ecosystems scale innovation across customers, partners, and technology providers,” says Rob McAveney, chief technology officer for Aras. “By joining the Alliance for OpenUSD, Aras is helping ensure that immersive digital twin environments and real-time simulation remain connected to the product and process truth managed across the lifecycle.”

Lifecycle-Connected Digital Twins for Omniverse

Through its participation in AOUSD, Aras plans to contribute capabilities that help industrial organizations operationalize digital twins built on OpenUSD and NVIDIA Omniverse libraries, including:

• Generating large-scale 3D visualizations of digital twins, product configurations, and digital-thread-connected datasets
• Linking 3D scenes directly to lifecycle data for drill-down traceability (e.g., geometry → configuration → change → validation evidence)
• Supporting live digital twin views that reflect operational updates and configuration changes 
• Augmenting immersive 3D environments with underlying product structures and related data including electronics and MBSE models
• Providing a scalable bridge between PLM-governed product data and real-time simulation environments

About Aras

Aras provides a digital thread platform for product lifecycle management and engineering AI. It is built on an AI-native, low-code foundation.

Sources: Press materials received from the company and additional information gleaned from the company’s website.