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.

Protolabs’ factories in the United States are ITAR-compliant and hold AS9100 certification. Each aerospace customer has access to dedicated engineering support, customer service teams, and manufacturing expertise. AS9100-certified machining in the U.S. and Europe allows aerospace companies to localize their supply chains.

“Space Foundation is a leading organization for one of our most important industries at Protolabs. I look forward to connecting with its members as we stay on the cutting edge and continue powering aerospace innovation,” says Suresh Krishna, Protolabs CEO and president.

Protolabs serves aerospace companies through its own facilities as well as its network of vetted manufacturing partners around the world. The hybrid business model taps into the speed and automation of its homegrown factories along with the expanded capabilities and cost efficiencies of its manufacturing partners, the company reports.

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

About the Course

The ASTM Professional Certificate Course in Additive Manufacturing has eight modules that cover all general concepts of the additive manufacturing (AM) process chain. For flexibility, the course is scheduled such that two modules will be covered every week to complete the entire course in one month.

This course will equip attendees with core technical knowledge related to common AM practices and will allow them to earn a Professional AM Certificate that will serve as the foundation and prerequisite for earning future specialized role-based AM certificates through the ASTM AM CoE. Attendees will complete a multiple-choice exam upon course completion.

Who Should Attend?

Whether just learning about AM or if you have experience and want to advance your knowledge, the General Personnel Certificate Course may be beneficial. The course is recommended for technicians, managers, engineers, and other individuals from government agencies, industry, and academia with any level of AM experience.

This program is designed for professionals who need a structured, standards-aligned understanding of AM.

After four weeks, participants can:

• Evaluate where AM fits within their organization.
• Communicate clearly about materials, processes, and trade-offs.
• Navigate qualification, inspection, and safety considerations.
• Contribute to decisions that affect cost, risk, and performance.
• Instruction is delivered by subject matter experts from organizations including NASA, Pratt & Whitney, and the University of Texas at Austin.

Participants receive a recognized ASTM certificate and a foundation for engagement with AM standards and certification pathways.

To register, click here. 

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

The report offers insight not at the part level alone, but across part, system, and enterprise levels. It draws on 6 years of sustained observation across both sides of the AM ecosystem — technology developers and manufacturing users. 

The report was presented to and discussed with AMGTA's global membership at the 2026 Annual Member Summit, held April 13 in Boston, alongside the companion Strategy 2030 document.

Standard cost comparisons of additive manufacturing against conventional manufacturing capture the same direct production costs on both sides while systematically excluding costs that conventional manufacturing embeds as invisible background — tooling capital committed before demand is known, inventory carrying costs, minimum order quantity waste, and obsolescence write-offs. The result is a structural bias that makes AM appear more expensive than a complete evaluation would show, according to AMGTA.

The report identifies this as a framing and measurement problem and provides the evaluative structure organizations need to conduct complete comparisons across all three levels at which AM creates value.

“The technology is proven. But the current adoption curve doesn’t reflect it—and one major reason is that the industry has been evaluating AM against a standard that was never designed to capture what AM actually changes,” says Sherri Monroe, executive director of AMGTA. “This report is the result of six years of watching that gap play out across industries, applications, and geographies. It is the argument the industry has needed and that only an organization with no commercial interest could make.”

“When I founded AMGTA, the goal was to create something the industry didn’t have: an independent, non-commercial voice that could make the case for AM’s value in the rooms where the real decisions get made,” says Brian Neff, chair of the AMGTA board of directors. “This report is that voice. It makes the argument we’ve been building toward—complete, rigorous, and designed to hold up under scrutiny from finance, procurement, and policy.”

The report is available at www.AMGTA.org. The companion Strategy 2030 document—What We Do and Why Membership Matters—is available to AMGTA members.

About the Additive Manufacturer Green Trade Association (AMGTA)

The Additive Manufacturing Green Trade Association (AMGTA) is a global, independent organization. Founded in 2019, AMGTA convenes technology developers, manufacturing users, and ecosystem partners across five continents to establish evidence-based understanding of where and how additive manufacturing strengthens resource and operational performance.

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

“In metal AM, quality isn’t a final inspection activity—it’s a production capability,” says Marten Jurg, co-founder and CEO of Additive Assurance. “The goal is simple, give teams the confidence to scale, by putting reliability and repeatability at the heart of the build process, not at the end of it.”

Additive Assurance is showing how AMiRIS moves beyond generic “monitoring” toward assurance that supports decisions (what to release, what to hold, what to audit, and what to investigate) without forcing plants into an overhaul of existing quality systems.

AMiRIS is designed to create usable evidence that Quality, Operations, and Certification stakeholders can act on, trace, and defend, according to Additive Assurance.

The company’s team combines deep expertise across additive manufacturing, aerospace engineering, physical metallurgy, computational materials science, computer vision, machine learning/AI, and software development, built around one mission: unlock the production potential of L-PBF by making quality repeatable and scalable, not artisanal.

At RAPID, Additive Assurance is on the show floor at Booth #2636

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

Via this acquisition, EOS says it strengthens its strategic focus on materials, in response to the demand for titanium additive manufacturing (AM). The integration of Metalpine’s capabilities enables EOS to expand access to titanium powders produced through Metalpine’s atomization process.

“For many years, Metalpine has been a strong and innovative partner to EOS,” says Joachim Zettler, chief technology officer of EOS. “By integrating Metalpine into EOS, we are taking the next logical step in our collaboration, strengthening our metal materials supply and accelerating innovation, particularly in titanium, where we see significant and sustained market demand.”

The acquisition strengthens EOS’ ability to deliver integrated materials, parameters, and process expertise, assisting manufacturers in qualification, improved process stability, and scaleingadditive manufacturing into serial production. Industries and applications that rely on titanium (aerospace, medical, and high‑performance industrial applications) now garner greater access to high‑quality powders, EOS says.

Metalpine will continue to operate as an independent company within EOS, maintaining its brand, organizational structure, and business operations. The company will continue to serve its global customer base.

“During the past few years, we have built a rock-solid foundation with EOS,” says Gerald Pöllmann, CEO of Metalpine. “Becoming part of EOS is a natural progression of this partnership, enabling us to further develop our technologies and scale our capabilities while continuing to reliably serve customers worldwide.”

“Our patented process stands for exceptional powder quality and consistency," says Dr. Martin Dopler, chief technology officer and head of R&D at Metalpine. "As part of EOS, we will further advance material innovation and support the growing requirements of industrial additive manufacturing, while continuing to provide our products to a broad market.”

About Metalpine

Metalpine GmbH is a AS/EN9100 certified, Austrian manufacturer, of high-quality metal powder for additive manufacturing and other advanced industrial applications. Headquartered in Graz, the company specializes in delivering highly spherical, pore-free metal powders produced using a proprietary wire-based gas atomization process, providing consistent quality, excellent flowability, and low oxygen content. With a strong background on research and development, Metalpine supports demanding industrial applications through a broad portfolio of alloys with a focus on titanium and copper, enabling reliable and serial industrial additive manufacturing for customers worldwide.

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

“Manufacturers are seeking more applications for additive manufacturing, and that’s exactly what these innovations are designed to provide,” says Rich Garrity, president, chief business unit officer. “Whether it’s designing tools faster, producing high-performance parts, or getting more accuracy out of production systems, we’re giving teams practical ways to put additive to work every day.”

GrabCAD Software + Additive App Suite

The new Additive App Suite, developed by Stratasys’ software partner trinckle, expects to launch later this summer with 10 apps, which were being demoed at RAPID+TCT 2026. Stratasys and trinckle plan to expand the number of apps available to 15 apps by November. Automated design apps for industrial applications such as Clamping Jaws, Shadow Boards, and Drill Guides will be embedded directly into GrabCAD Print and GrabCAD Print Pro. This integration enhances the overall interoperability within a single workflow session. Flexible licensing models provide individual and enterprise options.

The suite enables generation of production tooling. By embedding these apps directly into GrabCAD Print and GrabCAD Print Pro, Stratasys expands AM adoption beyond specialized AM teams to engineering, quality, and operations.

PolyJet Performance with J850 Core

The J850 Core printer expands the PolyJet technology lineup with a lower-cost system built for engineering teams focused on functional prototyping, Stratasys explains. It gives access to PolyJet performance and materials without paying for full-color capabilities. The system is planned to be open for booking by the end of April.

This system is suited for producing enclosures, housings, jigs, fixtures, and other functional components. With support for rigid, flexible, transparent, and PolyJet ToughONE materials, along with a large build tray and high-speed print modes, the J850 Core enables faster iteration.

“The J850™ Core printer is built for how PolyJet is used today by engineering teams that need to move fast and validate parts every day,” says Garrity. “It brings the performance and material capabilities customers expect, at a practical price point that supports the ability to scale across more teams and more applications.”

P3 MED Silicone 25A for Biocompatible Applications

Stratasys and Shin-Etsu are introducing P3 MED Silicone 25A, a biocompatible silicone for 3D printing patient-specific medical devices and low-volume production parts, available exclusively on Origin printers. Fully certified to ISO 10993 standards, the material delivers silicone properties such as elasticity, durability, and resistance to heat, chemicals, and aging. 

The material enables scalable production of anatomically precise devices like hearing aids, CPAP masks, orthotics, and prosthetics. By combining Stratasys’ additive manufacturing expertise with Shin-Etsu’s silicone science, the P3 Silicone line provides injection-molding-grade parts with precision.

SAF PA12 – Powered by Evonik

The new SAF PA12 enables production-grade performance, according to Stratasys. 

Stratasys’ new SAF PA12 - Powered by Evonik provides a PA12 solution for industrial production without requiring additional licenses, hardware, or process changes. 

At RAPID+TCT 2026? View these new materials at the Stratasys booth, #1601.

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

Using text prompts or uploaded images, Meshy's AI generates print-ready 3D models in seconds — automatically handling mesh repair, geometry optimization, and material compatibility checks that would otherwise require specialist knowledge or hours of manual work, the company says.

"By partnering with Form Now, we are completing the generative AI loop," said Ethan Hu, Founder and CEO of Meshy.ai. "Our users can already create stunning 3D assets with text in seconds; now, they can hold those assets in their hands with the same level of ease."

Available to all Meshy users since April 8, the Form Now integration connects Meshy's AI model generation directly to Formlabs' professional on-demand 3D printing service. Users who generate a model inside Meshy can export it to Form Now with a single click, select their material and color, and receive a professionally printed part at their door in as little as 48 hours — without leaving the Meshy workflow.

After generating and customizing a 3D model in Meshy, users click "Print with Form Now" to send their design directly to Formlabs' platform. The entire process — from AI generation to order placement — takes under five minutes.

Meshy's Workspace 3.0 — already live — is a ground-up redesign built around one goal: getting users from idea to 3D asset faster. Three core upgrades define the release:

• Instant Creation: A new creation bar with prompt presets lets users start generating without navigating away from their project — reducing friction from intent to output.

• Task-Oriented Workflows: Dedicated spaces for Image, Model, Print, and Animate keep users in a single context rather than context-switching between tools. Each space is optimized for its task, from AI image generation through to print-ready export.

• Unified Assets: Images and 3D models now live in a single asset library — no tab-switching, no lost files. Teams browsing 2D references and 3D models work from the same interface.

For design teams, manufacturers, and hardware partners, Workspace 3.0 also delivers production-grade API access with the stability and uptime that enterprise integrations demand.

Expanding the AI-Hardware Ecosystem

xTool is building its own creative tools on top of Meshy's APIs, leveraging its multi-color model conversion and printability repair capabilities. Additional hardware partners are in active integration with Meshy's platform ahead of upcoming product launches — including full-color 3D printing lines planned for later in 2026.

What we've learned from working with hardware partners — and from watching users at shows like TCT — is that the magic moment isn't the model on screen. It's the physical object in their hand," said Johnny Li, Head of 3D Printing Products at Meshy.ai. "That's what we design toward. Every product category in Creative Lab, every printability optimization in Workspace, exists to make that moment reliable, repeatable, and accessible to someone who has never touched a slicer in their life."

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

“It’s a transformable wearable, based on Iron Man 3 [released in 2013],” says Davenport. “In the movie, the suit transforms to fit Iron Man’s body. It is different from the medieval armor pieces that usually need a couple of helpers to put on.” 

Lend Me a Hand, Iron Man

The first step in Davenport’s Iron Man gauntlet design was getting a digital scan of his own hand. He started out with a LiDAR scanner, but swiftly discovered more affordable methods. “Nowadays even the front-facing camera on your smartphone or photogrammetry is an option,” he says.

He wasn’t in a position to call up Walt Disney Co., the movie’s IP owner, and ask for reference 3D CAD designs. Besides, he points out, “What you saw in the movie was made possible with visual effects. It wasn’t reality. So I had to fill in the gaps to make it function like that in reality.”

Zachary Derman in the Iron Man suit he made using 3D-printed pieces. Image courtesy of Zachary Derman.

Davenport owns two 3D printers, the Bambu P1S (~$400) and P2S (~$550). Bambu markets its P series printers as all-around affordable printers for consumers, makers, and hobbyists. The company also offers the H series printers, meant for personal or professional manufacturing tasks. He usually uses PLA Pro filaments, available from Amazon and other online sellers. 

Davenport also bought off-the-shelf electronic parts to drive the self-assembling features of the gauntlet. “The goal is to make it easy for anyone watching my videos to make the gauntlet themselves,” he says. “So anybody can take a look at my part list, buy what they need, get them shipped to themselves, and print the gauntlet themselves.” 

The Iron Man Gauntlet went through nearly 10 major iterations. But for smaller components, such as mechanical joints connecting different parts, the iteration count might be much higher. 

Davenport uses SOLIDWORKS CAD at work, but for his superhero costumes, he uses a combination of Autodesk Fusion 360 and Autodesk Blender. “Both [SOLIDWORKS and Fusion] CAD programs are parametric, but with Blender, it feels more like sculpting,” he notes. “I spent roughly seventy percent of my time in Blender for the Iron Man Gauntlet project.” 

Some parts began as parametric 3D parts, but they ultimately ended up in Blender for final touchups. To print the pieces, Davenport exported STL files from Blender. 

In total, Davenport estimated he’d spent “at least a few hundred hours” on the gauntlet. “It’s a lot more art than science,” he recalls. He’s not quite finished yet. “My next challenge is to add a missile to the gauntlet, so it could pop out from under a lid when I want it. I don’t want to use a button; it should be gesture-activated,” he reveals. But for safety reasons, he said the missile would be purely decorative; it wouldn’t actually fire. 

Exploded view of the Iron Man Gauntlet, designed for 3D printing. Image courtesy of Tommy Davenport.

Bring Me the Mandalorian’s Head

Five years ago, Zachary Derman decided to go after the Mandalorian’s head. Specifically, he wanted the helmet of the legendary bounty hunter from the Star Wars universe. Back then, he hadn’t yet learned 3D modeling, so he got the 3D file of the helmet from Thingiverse, a downloadable 3D content site. That kickstarted his superhero costume-making passion. Later, he would improve on the helmet with another file found on Do3D, which maintains a catalog of print-ready files, downloadable for a fee. 

“I use a software program called Armorsmith Designer [editing software for cosplayers, developed by Armored Garage]. I put in my body measurements, then got a 3D version of myself. Then I upload the downloaded 3D file and resize it until it fits before I print it,” explains Derman. 

Currently a college freshman studying mechanical engineering, Derman is learning to use Autodesk Fusion 360. For modeling superhero costumes, he relied heavily on YouTube tutorials from other cosplayers and makers. “I was able to model the Iron Man face plate, and the mask of the Invincible [a superhero character created by Robert Kirkman and illustrated by Cory Walker and Ryan Ottley, for Image Comics],” he says. “I designed them in Fusion 360.”

Still a newbie at CAD modeling, he frequently got stumped. “I modeled half of the Invincible mask, then I didn’t know how to use the mirror command to create the other half,” he recalls.

Derman’s first 3D printer was the Creality Ender 3 ($200 on Amazon). He subsequently upgraded it to Bambu Lab ($400 from Bambulab.com) and ELEGOO Neptune 3 Max ($350 on Amazon). With a mix of downloaded files, some CAD modeling, and home-based 3D printing, he managed to produce a Wolverine suit and Iron Man suit, among others.

“When I was making the Iron Man suit, I didn’t have the Neptune 3 Max [with bigger print capacity]. So I had to print my part in pieces and solder them together,” says Derman. 

Zachary Derman’s 3D-printable Invincible mask design, a work in progress in Autodesk Fusion 360. Image courtesy of Zachary Derman.

On Quora and Reddit, superhero fans discussed how much their favorite superheroes’ suits might cost, hypothetically. One Iron Man fan pointed out the life-saving arc, a critical piece in Iron Man’s suit, would cost $36 million. “I can make the Mandalorian helmet for $10, with a single roll of filament,” says Derman. But with iterations and experimentation, the cost of filament and paint could add up. “Paint is $15 a can, and I would go through five or six [for a single project]. And definitely don’t underestimate the time it takes,” he adds. 

Derman had to put in time and labor to sand the printed parts and paint them to give them a metallic finish. In newer versions of his Wolverine suit and a brand new Superman suit, Derman plans to incorporate cut foam pieces to create muscle layers, so he’s now learning to sew. He’s also looking for brands that might be interested in sponsoring his costume-making videos on TikTok and Instagram. 

On Instagram, Derman (@zachderman3d) posted a video, wearing his Iron Man suit. “I’m trying to get invited to the Avengers: Doomsday premiere. As you can see, I have my own Iron Man suit. It’ll be a dream come true for me. I love making costumes. It would be awesome to show up in one of these,” he made his case, hoping to get the attention of the film’s PR firm or a Marvel Comics executive. On TikTok, he made the same pitch, but wearing his bright yellow Wolverine costume. 

The announcements arrive as HP Additive Manufacturing Solutions (HP AM) marks a decade of innovation in additive manufacturing, reflecting the company’s focus on enabling digital manufacturing through an integrated ecosystem of hardware, materials, software and workflow technologies.  

As part of its advancements, the HP Jet Fusion 5600 series is introducing a High Productivity print mode that supports HP AM’s strategy to lower cost per part at scale.  The 5600 will support HP 3D High Reusability PA 12 Glass Beads, enabling the production of stiff, dimensionally stable parts at a low cost while advancing the range of applications customers can address with HP’s polymer additive manufacturing technologies.   

HP is also introducing HP Multi Jet Fusion Dual Tone technology, enabled by HP’s agent capabilities, which allows printing in two color tones—white and gray—to create special part features such as textures, QR codes, markings, and labels. The HP Jet Fusion 5600 series will be the first system in the HP portfolio to offer this capability built in, with availability planned for late 2026. 

“As we mark a decade of innovation in additive manufacturing, these latest advancements across our portfolio reflect HP’s focus on bringing industrial-grade capabilities closer to where ideas take place,” says Alex Moñino, senior vice president and general manager, HP Additive Manufacturing Solutions. “By lowering cost per part and simplifying workflows, we are making it easier for customers to adopt additive manufacturing and scale it across new applications."

HP Expands Access to Industrial MJF Tech 

HP introduces the HP Multi Jet Fusion 1200 3D Printer Solution, a compact system designed to bring access to HP’s industrial Multi Jet Fusion (MJF). The solution delivers the same core MJF technology used across HP’s existing additive manufacturing portfolio in a smaller, more affordable printer. 

The platform also serves as a scalable entry point into the existing MJF portfolio. The full solution will be available from early 2027.  

Solution for the HP Multi Jet Fusion 1200 3D Printer  

To streamline adoption, the solution is supported by an ecosystem of hardware and software designed to simplify the additive manufacturing process. Included with every HP MJF 1200 3D Printer is Magics Print for HP, a build-preparation software powered by Materialise, as part of the CO-AM Ecosystem. This tailored solution provides professional-grade tools for nesting, part orientation, and build layout.

Availability of HP IF 600 HT in the US and Canada 

HP also announced the general availability in the United States and Canada of the HP Industrial Filament 3D Printer 600 High Temperature (HT), first introduced in November 2025. The industrial filament platform is designed to support high-temperature materials and applications across sectors including aerospace, oil and gas, medical, automotive, and industrial manufacturing. 

HP Expands Metal Jet Applications 

HP Additive Manufacturing Solutions continues to expand the capabilities of its HP Metal Jet platform through the development and qualification of new materials for industrial production across high-growth sectors such as aerospace, tooling and energy. These include copper for high conductivity applications (thermal management and electrification), nickel-based superalloys such as M247LC for high-temperature aerospace components, and tungsten carbide–cobalt (WC-Co) materials for tooling.  

HP Additive Manufacturing Solutions also announced a collaboration with metal powder handling specialist, Volkmann GmbH, to introduce the vPort, a contained powder management system for the HP Metal Jet S100 Printing Solution. 

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

The award also includes funding to advance Beehive’s 100 lbf Frenzy 6 engine, beginning with the manufacturing of a First Engine to Test (FETT) asset and options for further testing, vehicle integration, and flight demonstration.

The award, managed through the SOSSEC consortium, supports a Small Expendable Turbine (SET) — Family of Affordable Mass Munitions (FAMM) prototyping effort led by the Air Force Life Cycle Management Center (AFLCMC). The SET engine program is a component of the U.S. Air Force’s strategy to develop, produce, and qualify low-cost, disposable jet engines for uncrewed aerial systems and standoff systems.

FAMM is a FY2026-focused, Pentagon-wide initiative designed to shift from high-cost, low-quantity weapons to a large-scale, cost-effective arsenal. Beehive uses additive manufacturing to produce low-cost jet engines at high speed, and its Frenzy engine is specifically designed for mass-produced munitions and swarm-class drones.

“Beehive is honored to partner with the U.S. Air Force in redefining the speed of defense. By harnessing additive manufacturing to collapse complex supply chains into scalable, 3D-printed propulsion, we are providing the ‘affordable mass’ essential to modern deterrence,” says Gordie Follin, chief product officer at Beehive Industries. “This collaboration ensures our warfighters willhave the high-volume, mission-ready capabilities they need to maintain a competitive edge in any theater.”

Over the past year, the team validated the Frenzy 8 engine through ground testing and high-altitude testing in record time while demonstrating scalability. Beehive also launched a “Pathfinder” program to validate production scalability and the results proved out Beehive’s path for mass engine production starting this year, the company reports.

By balancing testing milestones with a production ramp-up, Beehive demonstrated its transition from a development-focused company to a production-ready propulsion provider.

About Beehive Industries

Beehive Industries is a U.S.-based manufacturer specializing in the design and development of advanced, additively manufactured jet engines for uncrewed aerial defense applications. Beehive delivers propulsion systems with speed, affordability, and scalability.

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

Tectonic-3D refers to itself as a high-end material supplier, moving beyond standard filament production to offer advanced molecular-level engineering for industrial sectors. The company specializes in high-performance polymers—including carbon-fiber reinforced, bio-based, and flame-retardant composites. The partnership builds upon a history of collaboration in developing defense-specific applications. 

UltiMaker and Tectonic-3D support 10 dedicated print profiles within the UltiMaker Marketplace. These profiles are fully optimized for the UltiMaker S series and the Factor series. These preconfigured settings allow users to achieve industrial-grade results with Tectonic-3D’s specialized carbon fiber-reinforced and flame-retardant materials.

“Tectonic-3D’s commitment to advanced material innovation perfectly complements our mission to provide a reliable, industrial-grade production platform,” says Jim Franz, president of the Americas at UltiMaker. “By becoming the exclusive U.S. distributor, we are giving our customers direct access to the most advanced filaments on the market, backed by the seamless integration and support they expect from UltiMaker.”

UltiMaker will carry the full Tectonic-3D filament portfolio, including:

• KRATIR Series: High-performance Carbon Fiber (CF) and Glass Fiber (GF) reinforced polymers (PET, PA, and PP) designed for extreme stiffness and heat resistance.
• ZEPHYR Series: Bio-based, ultra-lightweight structural materials optimized for drone and aerial applications.
• VULCAN Series: High-temperature, flame-retardant filaments including PEI (Ultem™), PEKK, and PPSU for the most demanding environments.
• ANASA Series: Biocompatible and flexible TPC materials for medical and ergonomic applications.

“We look forward to building upon our partnership with UltiMaker,” says Ken Kempinski, CEO and co-founder of Tectonic-3D. “Our materials are designed to push the boundaries of what is possible in additive manufacturing. We are extremely excited to expand our collaborations with Ultimaker on their S and Factor series printers.”

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

Model-Based Enterprise

Edition 4 of the newest application protocol 242 (AP242) for 3D engineering was published in 2025 and established a system for tagging data objects with persistent identifiers (ID) for traceability of product data across different manufacturing systems and suppliers. Complete persistent ID support has been incorporated into release 8.0 of the Kubotek Kosmos libraries allowing traceability of product data from CAD formats as it moves into standard STEP files used in various applications across manufacturing and inspection.

To aid recipients in understanding a complex part, 3D product definitions commonly include saved view definitions containing camera orientation, zoom scale, and sets of annotations related to that view. These views are supported in the STEP AP242 standard and the 8.0 release expands toolkit support for such views. 

For 8.0 the Kosmos API also added support for reading STEP XML, the Part 28 STEP format for storage of complex product assemblies. STEP XML is commonly used for long-term archiving of 3D product designs in Aerospace and Defense industries.

Connecting 3D Quality Systems with QIF

Support for reading precise part models, connected product manufacturing information, and their persistent IDs from QIF files is now included in the 8.0 version of the 3D Framework libraries. The Kosmos libraries support enrichment of such model-based datasets, with native objects for annotations and dimensions, planes, points, centerline, etc. as well as semantic PMI.

Digital Thread-Focused Modeling Kernel

The 8.0 release continues to improve on the toolkit’s established modeling capabilities and performance in various areas such as Boolean volume operations, skinning through profiles, sweeping profiles through space, and imprinting curves along a vector onto surfaces.

Support for adding fillet and round features to solid models (aka blending) has grown in 8.0 with handling of capping of multiple faces adjacent to the new blend face. Blending operations often change model topology and legacy modeling kernels commonly generate new internal IDs for the resulting edge and face objects. Maintenance of object IDs even as the Kosmos kernel expands to handle more sophisticated blending cases supports digital thread connectivity to the original model.

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

Working Model 2D is a 2D kinematic and dynamic simulation tool used by engineers and adopted colleges and universities across six continents. Users build mechanical systems on screen, run physics simulations, and measure forces, velocities, and accelerations on bodies or joints.

Version 10’s AI assistant connects to the simulation engine through the Model Context Protocol (MCP). Users describe a mechanism in plain English—for example, “Build a slider-crank with a 5 cm crank and a 15 cm connecting rod”—and the assistant creates the bodies, joints, and motor and runs the simulation. The assistant can also interpret photographed hand-drawn sketches to generate working models.

“The AI assistant handles the setup work so users can focus on analyzing results and refining designs,” says Alan Wegienka, president of Design Simulation Technologies. Wegienka further explains how the new automation interface makes the simulation engine accessible from C++, C#, Visual Basic, and Python, noting that included Python bindings support parametric studies and automated workflows. Also, built-in script editors for Python and VBScript give syntax highlighting and error reporting inside the application.

Version 10 also adds real-time data exchange with Excel, MATLAB, Python, and user-written DLLs during running simulations. Working Model 2D can function as a plant model for Simulink co-simulation.

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

“CAM programmers rely on accurate cutting tool data to ensure efficient and reliable machining," says Meir Noybauer, business development officer at ISCAR. "Supplying our tooling information in the MDES format makes it easier for CAM systems to provide that data to their users, helping manufacturers reduce programming time and machine complex parts with greater confidence.”

“By making high-quality tooling data easily accessible to CAM and CNC systems, ISCAR’s adoption of MDES is an important step towards more integrated manufacturing," says Dr. Yavuz Murtezaoglu, founder and managing director of ModuleWorks. “With more companies adopting MDES, we are seeing a rapidly growing ecosystem where data flows efficiently and reliably across the entire digital manufacturing chain.”

ISCAR joins other companies using MDES to improve interoperability across CAD, CAM and CNC environments, including Autodesk, BobCAD-CAM, CHIRON Group, Cimsource, DN Solutions and PTC, among others.

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

At RAPID + TCT 2026 (Booth #1801), April 13-16, 2026 in Boston, 3D Systems will introduce the SLA 825 Dual to the U.S. market, a next‑generation stereolithography system engineered to boost production output while maintaining accuracy and consistency needed for industrial manufacturing, 3D Systems reports. The company is also unveiling AddiTrak, a factory‑floor software platform built for managing connected 3D Systems production environments, according to 3D Systems.

These innovations reinforce 3D Systems’ strategy to deliver production‑ready additive manufacturing solutions that integrate hardware, software, materials, and application expertise to move beyond prototyping and into scaled manufacturing.

Maximizing SLA Speed, Productivity

The SLA 825 Dual features a 22% larger build volume and delivers up to 25% faster build speeds than its predecessor. Made for high‑utilization environments, the system supports applications such as motorsports, full-scale precision prototyping, and investment casting patterns

By combining higher throughput with SLA precision, the SLA 825 Dual provides a scalable solution for transitioning stereolithography into high-volume production workflows, according to 3D Systems.

Driving Efficiency with AddiTrak

3D Systems announces AddiTrak, a secure, on‑premises software platform designed for the entire range of 3D Systems printers and accompanying workflows. AddiTrak provides centralized monitoring, analytics, and optimization across the production floor through a unified dashboard, while supporting Industry 4.0‑compatible connectivity.

AddiTrak is fully native to the 3D Systems ecosystem and integrates with 3D Sprint, enabling a connected, end‑to‑end workflow. AddiTrak is fully on-premises and under customers' control, through all stages of the manufacturing process.

Real‑World Production Examples

3D Systems will highlight customer application examples that demonstrate additive manufacturing supporting production. One of these is Eureka Pumps AS, based in Norway, which has partnered with 3D Systems to manufacture large-format metal spare parts on demand, using 3D Systems Direct Metal Printing technology.

“The industrialization of additive manufacturing continues to accelerate as more companies realize its ability to deliver both performance gains through design innovation and operational flexibility through digital production,” says Patrick Dunne, senior vice president, Technical Fellow, 3D Systems.

“Over the past several years, we’ve made disciplined investments to refresh our portfolio and focus on manufacturing applications where additive delivers the greatest value,” says Dr. Jeff Graves, president and CEO, 3D Systems. “At RAPID + TCT 2026, we’re demonstrating how those investments are translating into production-focused solutions, from high‑throughput stereolithography to connected software platforms that improve visibility and control across the full range of factory environments.”

Sessions During RAPID + TCT

Conference attendees are also invited to attend the following presentations:

• Steve Hartung – Pixels to 3D Printed Investment Casting Patterns, Tuesday, April 14, 2026, 1:30 PM, Tech Hub Stage, Booth #1531
• Joe Wisnewski – AddiTrak, Tuesday, April 14, 2026, 3:30 PM, Tech Hub Stage, Booth #1531
• Panel: Life‑Saver: How AM is Transforming Point‑of‑Care, including Dr. Jeff Graves, Wednesday, April 15, 2026, 8:30 AM, SME Main Stage

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

Attendees will have opportunity to exchange ideas, acquire the latest knowledge, and explore the future of modeling, analysis, and simulation at this biennial event. In addition, guests can learn about industry trends, real-world insights, and the technologies influencing the engineering landscape of the future.

Major session themes include the following and more: 

• AI and Machine Learning
• Systems Modeling and Simulation
• Simulation Process and Data Management
• Simulation Driven Design
• Multiscale and Multiphysics 
• CFD
• Electromagnetics
• Biomedical
• Physical Testing and Numerical Simulation

The Conference will also feature a range of live in-person engineering simulation training courses presented by NAFEMS Tutors Tony Abbey and Sean Teller.

Keynotes are as follows: John Linford of NVIDIA, "Accelerating Industrial Engineering: From Product Design to Manufacturing in the AI Supercomputing Era;" Brett Soltz of The Aerospace Corp., "IV&V at The Aerospace Corporation in Support of U.S. Space Force Programs;" Heather Oravec of the NASA Glenn Research Center, "Engineering Mobility Beyond Earth: Simulating Terrain and Reinventing Rover Wheels;" and Garrett Swindlehurts of General Mills, "Pacing with the Exponential Progress of AI in the Food CPG Industry."

Who Should Attend

The event is open to anyone in the simulation space, especially:

• Simulation Engineers & Analysts
• Engineering Managers & Directors
• Methods Developers & Researchers
• Technology Providers & Executives

The agenda can be found here.

Registration Details

• Member Rate: $995 
• Non-Member Regular Rate: $1375 

Also, feel free to reach out to bel.hooley@nafems.org and ask about our credits + cash pricing!

​tudent Opportunity Registration:

NAFEMS also offers 5 free student registrations for the NAFEMS Americas Conference. Email kathy.elliott@nafems.org directly for details.

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

With natural language prompts, many of these hindrances—and the safeguards—are expected to disappear. In this article, we examine the integration of large language models (LLMs) in design for additive manufacturing (DfAM) and computer-aided manufacturing (CAM) software to understand the new possibilities they usher in, along with the concerns they raise.

Natural Language Input is Not a Fad 

“Natural language input in software is becoming a trend. It’s not just an experiment,” says Bart Van der Schueren, chief technology and strategy officer, Materialise. “I’ve been very impressed with how well natural language models can read and interpret our APIs [application programming interfaces].”

Founded in 1990, Materialise offers 3D printing services and software aimed at aerospace, automotive, healthcare, and other industries. Its print preparation software Materialise Magics allows you to prepare, repair, and analyze complex parts for 3D printing, including lattice-rich geometries. It has APIs for automation and integration into broader CAD-to-print workflows.

“I’ve seen how you can use a GitHub copilot with natural language support to write Python code to create a shrink-wrap around a part,” says Van der Schueren. “It might not be doing the job in the most efficient way, but it’s making the correct API calls and writing executable code.” 

Shrink-wrapping, a print preparation step, involves creating a watertight outer shell for a complex part destined for printing. In modern additive manufacturing (AM) software such as Materialise Magics, the process is almost completely automated, driven by algorithms. 

The software can instantly analyze the uploaded part and generate the appropriate setup and estimates. Image courtesy: Toolpath

“In the future, I expect AI-powered copilots will not only propose code but can take action,” says Van der Schueren. “But you need a user with domain knowledge to ask the right questions to get meaningful feedback.” 

Teaching the Chatbots to Speak Manufacturing

Consumer-friendly chatbots like ChatGPT are well-trained in everyday use of natural language, but chatbots for DfAM or CAM software need an additional layer of domain-specific knowledge. Terms like pressure, load, stress, walls, and support mean specific input parameters and geometric features in manufacturing software, quite different from how they might be used in everyday conversation. 

“In our software like CO-AM Brix, the scripting and workflow setup could be increasingly driven by AI. And with our software Mimics, where users create macros to combine certain workflows, large language models would make the job a lot easier. But to do this reliably, you need an orchestration layer around the API, something along the lines of an MCP-style [Model Context Protocol] server to provide the right context and guardrails,” says Van der Schueren. 

Materialise CO-AM Brix is low-code, node-based automation software. In November 2025, Materialise launched CO-AM Brix as part of its open, secure software ecosystem called CO-AM. Materialise Mimics facilitates medical operation planning, analysis, and execution using 3D-printed replicas of anatomical parts. 

The introduction of natural language input is not the death knell for the familiar menu bars and dialog boxes, Van der Schueren assures. Don’t expect a chat window with a microphone icon to become the entirety of your DfAM or CAM software UI. “In many cases, you will still need to dive into the code and change some lines. Your engineering skills are still very important, but LLMs will take on a role of growing importance,” he says.

The integration of LLMs and documenting how they interact with Materialise’s APIs, Van der Schueren believes, is too important to leave to third-party developers. “In a sense, the AI is reading the documentation we provided. It’s our proper documentation that allows the AI to do the job. So if we let others do it, there’s a risk that the documentation is not correct,” he says. 

Natural language input makes manufacturing software easier to use, but it also places new burdens on the user. If you ask a DfAM software to reduce the thickness of a part’s wall, the software will most likely obey. “Then it’s up to you as an expert to know, with the machine you have, what the minimal wall thickness is [so] you can build,” says Van der Schueren. 

A CAM System as a Chessboard

In 2024, Al Whatmough, a veteran of the mechanical CAD software industry, joined the startup Toolpath as CEO. Trained in manufacturing rules, Toolpath’s AI can scan the geometry of a part and recommend the tools appropriate for the job. Whatmough compared automated CNC programming to playing chess. 

“A rook can only move in a certain direction. A knight can only move a certain way. Similarly, we have some default CNC rules baked into the engine,” he says. Based on these known rules, Toolpath can scan a part and tell the user, “This part cannot be machined with the tools you have. This part can be machined if you buy these tools,” he explains. 

To make these assessments accurately, Toolpath needs to know the equipment at the user’s disposal, its strength and limitations. If CNC were a game of chess, the AI needs to know the pieces in the player’s hands—pawns, knights, and rooks—and their positions on the board to offer a winning strategy. And that’s where Whatmough is looking to employ natural language processing. At the time of the interview, he was in the thick of a new feature’s launch. 

“LLMs have gone far beyond text. They can also interpret images. So natural language can help users say, these are the parts I’ve programmed in the past, and these are the pictures of the tools in my shop. So the AI can discover the tools the users have and apply the known rules,” he says.

He thinks of computer-digestible images as part of the natural language of CNC programming in the AI-driven workflow. Therefore, for him, the focus is less on ChatGPT-style text input, but more on 2D and 3D visuals the AI can decode and catalog. He pointed out users would have different preferences, which need to become part of the AI’s rules.

“One user might prefer to use the end mill on his machine; another might prefer to use the end mill in a shop. One might be against borrowing the necessary end mill from the shop; another might not care. One might prefer to buy the tool he needs from Kennametal; for another, it might be ISCAR,” notes Whatmough.

Currently, setting these priorities and preferences in CNC software would require the user to dig several menus deep into the dialog boxes, or write programming code. But Whatmough feels “describing these in natural language, or by dragging and dropping images would make more sense.”

Whatmough believes, in the foreseeable future, AI and natural language will make complicated CNC software easier to learn and use. But the software’s knowledge of the physical equipment and layout is a critical component that makes the AI more powerful. Without that, the AI will be cornered and checked. 

This Q1 2026 quarterly update delivers timely insights beyond the annual report, according to Wohlers Associates. It captures early-year developments across technologies, applications, and regional markets, while assessing how they align with or challenge the outlook presented in the 2026 edition.

Drawing on ongoing research, industry engagement, and market monitoring, this update highlights significant signals of the quarter and interprets their implications for decision makers navigating an evolving AM landscape.

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

Smart Automation

The new BigRep ONE.5X introduces advanced automation, a redesigned user interface, and smarter print performance.

“The ONE.5X represents everything we've learned from over a decade of industrial large-format 3D printing, as well as global customer feedback. All of that is distilled into a machine that delivers consistent results, regardless of an end-user’s experience level.”

— Jeff Olson, President, BigRep America, Inc.

The ONE.5X retains the dual PEX2 extruders, material compatibility, and flexible SWITCHPLATE print bed options from the previous ONE.5, while adding a new generation of features.

The ONE.5X introduces a new Human Machine Interface (HMI) — the same intuitive UI first seen on the BigRep VIIO 250. Over-the-air (OTA) software updates are also new to the ONE platform.

Advanced Automation 

The ONE.5X is engineered to reduce manual user intervention:

• XYZ autocalibration handles machine setup automatically, eliminating manual calibration steps.
• Auto-sequential printing detects available space on the print bed and starts the next queued job automatically — no operator required.
• Adaptive bed mesh leveling maps the print surface before every job, compensating for any unevenness to nail first-layer adhesion across the full bed.
• Relay Mode automatically switches to the second extruder if the first runs out of material, keeping long print jobs running to completion without operator intervention.

Built for Speed 

• Maximum print speed of up to 250 mm/s.
• A new pressure advance algorithm regulates material flow during acceleration and deceleration, eliminating corner bulges.
• Vibration compensation actively counteracts frame resonance at higher print speeds.

BigRep & Massive Dimension Collaborate

In other news, through a new co-development partnership with VT-based Massive Dimension, BigRep is expanding into pellet-based large-scale extrusion by integrating the company’s new MDX extruder into the BigRep ONE platform.

Weighing 25% lighter than its predecessor and built with 40% fewer parts, the MDX from Massive Dimension is compact and designed to be serviced fast, making it an ideal fit for the BigRep ONE. 

By combining Massive Dimension’s extrusion expertise – backed by more than a decade in large-format additive manufacturing – with BigRep’s industrial-grade printers and global sales and service network, the two companies aim to support customers looking for faster printing, lower material costs, and greater production flexibility, the companies note.

BigRep and Massive Dimension are targeting availability by end of 2026. The MDX extruder will be on display at RAPID + TCT, Booth #2355.

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

With greater than 90% light transmittance, this material enables engineers to additively manufacture complex, internally structured micro-scale devices.  BMF’s Clear opens the door to high-fidelity innovation and application in research and commercial environments.

One such opportunity lies in scalable manufacturing, where BMF Clear enables production of complex micro-scale devices and integrated optical features. This includes applications such as microfluidic lab-on-a-chip systems with fiber alignment channels, freeform micro-lenses printed directly onto fiber optic tips, chip surfaces or sensor arrays, and integrated waveguides or photonic interfaces for sensing and data communication. 

Designed for integration with Boston Micro Fabrication's 10-micron and 25-micron systems, BMF Clear prints at layer heights between 10 and 50 microns. This compatibility extends across BMF's platform range—from advanced, high-end systems to the compact, benchtop microArch S150 series—making it accessible for any lab or production environment. 

"The research and engineering communities have long been constrained by the lack of truly transparent material suitable for micro-scale 3D printing," says John Kawola, CEO of Boston Micro Fabrication. “For years, achieving optical clarity with high print fidelity has led researchers to rely on traditional methods like PDMS soft lithography, which limits scalability, durability, and design flexibility. BMF clear resin directly addresses this challenge, bridging the gap between prototyping and production-level micro-manufacturing."

BMF Clear's optical clarity and surface finish create optically clear, perfusable channels, which position the material to transform various industries including biomedical, photonics, and optics. BMF Clear’s transparency facilitates the creation of complex microfluidic devices and lab-on-a-chip systems, where precise visualization and analytical reliability are critical for processes such as cell culture and high-resolution droplet generation.

Within micro-optics and integrated photonics, BMF’s Clear Material supports the direct production of components like freeform lenses, complex waveguides, and other intricate optical interfaces directly onto fiber optic tips, chip surfaces, or sensor arrays. These applications are instrumental in producing advanced components, including fiber-to-chip couplers and complex microstructures for high-speed data communication, sensing, and imaging applications.

Having passed biocompatibility tests for skin irritation, sensitization, and in vitro cytotoxicity, there is significant scope in biomedical settings too. Medical device engineers can leverage the clear material’s dimensional fidelity and clarity for device miniaturization, particularly in endoscopic systems, intraocular tools, and minimally invasive drug delivery systems where internal visibility and signal integrity are critical. 

BMF Clear is available through direct sales channels and the European distribution network of Boston Micro Fabrication. Pricing is available upon request. For more information, visit www.bmf3d.com.

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

 

The total consideration payable to the company in connection with the transaction is up to $12.5 million, consisting of a $2.0 million upfront cash payment, and up to $10.5 million of deferred payments tied to the future performance of the product lines over the next 12 months. Inspira has assumed operational control of the product lines, effective immediately. Completion of the transaction remains subject only to the receipt of customary regulatory approvals.

The Company believes this transaction represents a vital step as it advances its previously announced strategic alternatives review process to maximize shareholder value and reflects Nano’s continued focus on optimizing its cost structure, reducing operating complexity and lowering overall cash burn. After thorough review of the Company’s asset base, management and the board of directors determined that the AME and Fabrica product lines were not aligned with its go forward priorities. The Company expects this transaction to reduce annualized cash burn by approximately $10 million, to strengthen its liquidity and financial flexibility, and to enable greater focus on key strategic initiatives, according to Nano Dimension.

“Today’s announcement marks the first of a series of steps to maximize shareholder value and builds on the cost reduction actions initiated in the third quarter of 2025," says David Stehlin, chief executive officer. "The sale of the AME and Fabrica product lines will lower our operating costs and cash burn while reinforcing financial flexibility, and the deferred consideration structure allows us to participate in potential upside as the product lines perform under Inspira’s ownership.”

Nano Dimension will continue to evaluate strategic alternatives to maximize shareholder value and provide updates on its strategic alternatives review process as appropriate. The Company will update its 2026 financial guidance on its first quarter 2026 earnings call.

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

The HP Z8 Fury G6i is designed to meet demanding compute and AI workloads, HP reports. With support for up to four NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition GPUs and next-generation workstation Intel processors, the Z8 Fury is high performance for advanced AI development, visual effects, and simulation workloads, and is purpose built to be a host for sharing scarce GPU resources with HP ZBoost.

HP is also introducing the HP Max Side Panel for Z8 Fury and Z4 workstations, a chassis expander that increases internal volume. The new HP Max Side Panel allows power users to install larger graphics cards tool free, while maintaining thermal performance and serviceability.

Built for Complex Work Anywhere

For professionals who need workstation class performance on the move, HP has updated its mobile portfolio with the HP ZBook X G2i, HP ZBook 8 G2i, and ZBook 8G2a. These light mobile workstations deliver AI workstation performance, which includes AMD and Intel options, integrated or discrete graphics, scalable memory, and improved portability without sacrificing battery life, according ot HP.
 
The HP ZBook X is a 16-inch mainstream mobile workstation, and features 3000-level graphics and 128GB RAM. Together, the HP ZBook X and HP ZBook 8 enable architects and engineers to run complex, multi-software workflows wherever work happens.

Accelerating with HP Z Boost

HP is also bringing updates to HP Z Boost, a GPU sharing solution that turns workstations into on-demand, shared resources. Initially introduced for AI workloads, HP Z Boost is now expanding to rendering, helping increase GPU utilization and accelerate productivity without moving files, HP reports.

More information can be found here.

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

Berkebile, who most recently served as vice president and chief financial officer (CFO), has been a key member of the NCDMM executive team. He replaces Dean L. Bartles, Ph.D., who has served as the interim CEO for the past 6 months. In his new position, Berkebile will report to Dr. Bartles, who will remain president and CEO of MTDG as well as chairman of the NCDMM board of directors. Berkebile will also continue in his CFO role until a replacement is named.

“Gene has been instrumental in shaping NCDMM’s growth, strengthening its financial foundation, and advancing our strategic priorities,” says Dr. Bartles. “He brings a rare combination of mission focus, operational discipline, and forward-looking leadership. The Board has full confidence in his ability to accelerate NCDMM’s impact and position the organization for continued success in support of our nation’s defense industrial base.”

Berkebile joined NCDMM in 2007 as finance administrator and steadily advanced through roles, including manager and director of finance, before being named CFO. With more than 30 years of experience in government contract accounting and administration, he has led the development of internal budgeting processes and performance metrics, supported numerous successful program proposals, and overseen implementation of advanced financial systems. As CFO, he managed NCDMM’s government contract portfolio, currently valued at more than $238M.

Gene D. Berkebile, NCDMM

“I am honored to step into the position of CEO and to continue serving alongside the exceptional team at NCDMM,” says Berkebile. “This organization plays a critical role in strengthening the U.S. defense industrial base and advancing manufacturing innovation. I look forward to building on our strong foundation, deepening our partnerships, and driving mission-focused growth that delivers meaningful impact for our warfighters and the broader manufacturing ecosystem. I am grateful to the Board for their trust and confidence in my leadership.”

Prior to joining NCDMM, Berkebile held roles at Concurrent Technologies Corp. Throughout his tenure at NCDMM, he has played a key role in expanding the organization’s capabilities and supporting its growth in defense manufacturing and machining innovation.

Berkebile holds a bachelor’s degree from the University of Pittsburgh at Johnstown and a master’s degree in Business from Saint Francis University. He also earned a master’s certificate in Government Contract Management from George Washington University and is an active member of the National Contract Management Association, Pittsburgh, Pennsylvania Chapter.

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

The Accuris AI Assistant is grounded entirely in publisher-authorized content and returns answers with precise citations to the underlying clauses, the company claims. 

Built into Engineering Workbench and Accuris Thread, the AI Assistant eliminates the need to manually search and interpret dense standards. Engineers can ask questions in plain language and receive clear, clause-level answers tied directly to authoritative sources, according to Accuris.

“Engineering has reached a tipping point,” says Claude Pumilia CEO of Accuris. “The volume and complexity of standards and technical requirements have outpaced traditional ways of working. With the Accuris AI Assistant, we’re bringing together licensed content, traceability, and AI to fundamentally change how engineers interpret and apply critical information—faster, more confidently, and with full accountability.”

The Accuris AI Assistant acts as an intelligence layer across a user’s subscribed content. When a question is asked, it retrieves relevant information from authorized sources and generates a response grounded in that material.

Key capabilities include:

• Citation-backed answers linked to specific clauses and sections
• Clause-level question answering to pinpoint exact requirements
• Document summarization to accelerate understanding of complex standards
• Compliance insight to explain implications in plain language
• Copy-ready outputs with references for reuse across workflows

Because it’s embedded directly within Engineering Workbench and Accuris Thread, engineers can move from question to answer without breaking context.

Accuris AI Assistant operates within strict guardrails:

• Enabled only for licensed content where publishers have authorized its use
• No training on customer or standards organization data
• Access is restricted to content within each user’s active subscription
• Every response is fully traceable, with direct citations to source material
• Designed to support engineering judgment, without making decisions or approvals

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

2012–2014: Learning the Hard Way

The first annual Compendium of case studies appeared in 2013, documenting 25 hands-on engineering experiments. Each project tested whether complex CAE applications could run efficiently in the cloud.

Early results were sobering. Success rates hovered around 40% in 2013 and 60% in 2014. Engineers encountered roadblocks everywhere: slow onboarding, licensing friction, data transfer bottlenecks, configuration complexity, and the ever-present need for scarce HPC expertise.

Yet every case study concluded with two powerful sections: Lessons Learned and Recommendations. These weren’t marketing summaries. They were hard-earned operational insights from real engineers trying to get real work done. Those lessons would later become the seeds of SimOps (Simulation Operations Automation).

2015: The Software Container Breakthrough

A major turning point came in 2015. Based on patterns emerging from dozens of experiments, the team introduced novel HPC software containers tailored specifically for engineering workloads. Instead of installing and configuring simulation software on every cluster, applications were packaged into portable, ready-to-run containers.

The impact was dramatic. Onboarding time dropped from an average of three months to just a few days. Engineers no longer needed deep knowledge of system architecture or cloud infrastructure. Through a browser-based interface, they accessed what felt like a familiar remote desktop—backed by powerful bare-metal or virtualized HPC resources.

This abstraction between software and hardware removed one of the biggest operational barriers to cloud HPC adoption. It also quietly shifted the narrative: HPC was no longer just for specialists. It could become part of everyday engineering design.

Case Studies That Proved the Model

As annual Compendiums of case studies continued—eventually totaling 232 cloud-based engineering projects—the evidence accumulated.

At Rimac, for example, engineers designing some of the world’s fastest electric hypercars gained on-demand access to powerful cloud resources. Simulation cycles shortened. Design iterations accelerated. More sophisticated geometries and physics became feasible. And because cloud resources were elastic, they paid only for what they used.

In another study, marine engineers running NUMECA (now at Cadence) FINE/Marine simulations found that bare-metal cloud infrastructure provided performance advantages over local upgrades—without the overhead of maintaining in-house HPC expertise. Containers enabled immediate access to clusters without installation delays.

An implantable planar antenna simulation project demonstrated a fourfold speed increase compared to a local workstation. Preconfigured containerized ANSYS HFSS environments ran instantly, eliminating the traditional setup burden.

Across industries—automotive, marine, aerospace, medical devices—the same themes emerged:

• Access must be simple.

• Software must be ready-to-run.

• Infrastructure complexity must be hidden.

• Performance must be predictable.

• Operational friction must be removed.

The experiments were no longer about “Can HPC run in the cloud?” They were about “How do we make simulation operationally scalable?”

2013–2024: From Case Studies to Best Practices

As the Compendiums expanded—supported by industry leaders such as Ansys, Hewlett Packard Enterprise, Intel, and media partners including Digital Engineering DE 24/7 —the UberCloud initiative evolved into Simr, reflecting its broader mission: delivering simulation-ready infrastructure as a service.

By 2024, the experiment success rate had reached 100%. More importantly, the accumulated Lessons Learned across 232 projects were distilled into structured Best Practices. Patterns became frameworks. Recommendations became repeatable methods. Operational insights became a philosophy. That philosophy became SimOps.

Image courtesy of SimOps.

2024: The Birth of SimOps

In 2024, the SimOps initiative has been launched, positioning itself as “The DevOps of HPC.” The comparison is deliberate. Just as DevOps transformed how software is built and deployed, SimOps addresses how engineering simulations and HPC infrastructures are run, managed, and scaled. SimOps is not about software development. It is about operational excellence in technical computing.

SimOps provides guidance on:

• Workflow automation

• Hybrid-cloud HPC orchestration

• Containerization of simulation stacks

• Data lifecycle management

• Performance benchmarking

• Cost optimization

• Cross-functional collaboration between R&D and IT

It recognizes that simulation bottlenecks are rarely just about compute power. They are about process, governance, data management, cultural adoption, and operational repeatability.

Inspired by community-driven movements like DevOps and FinOps, SimOps was incorporated as an independent non-profit organization serving the HPC, AI, cloud, and engineering simulation communities. Today, SimOps offers:

• Training courses and certifications

• A SimOps software stack

• A Practitioner community platform

• Webinars and a growing podcast series

• A structured maturity model for simulation-driven organizations

A Cultural Shift in Engineering

What began in 2012 as a practical cloud experiment has evolved into a broader operational philosophy. The early HPC/Simulation experiments asked whether and how simulations could move to the cloud. Today, SimOps asks how simulations can become a scalable, automated, and reliable enterprise capability. Over twelve years, the journey revealed a powerful insight: The true challenge was never just compute performance. It was operations.

Simulation projects fail not because solvers are weak—but because workflows are fragile, data is often chaotic, onboarding is slow, licensing is complex, and collaboration between engineering and IT is often misaligned. SimOps addresses those systemic gaps.

From Experiment to Ecosystem

The story of SimOps is not one of a single product or breakthrough technology. It is the story of a community learning, documenting, refining, and sharing operational knowledge across more than a decade. From 25 case studies in 2013 to 232 cloud-based engineering projects by 2024, the trajectory reflects a maturation of both technology and mindset. What started as an experiment has become a movement.

And if DevOps reshaped software engineering, SimOps may well define the next chapter in simulation-driven innovation—where HPC, AI, cloud, and engineering simulation converge into operational excellence. The experiment worked. Now the operations scale.

Want to join the movement? Explore the best practices, start your SimOps Fundamentals training, get certified, and join the SimOps Practitioner Club at www.simops.com.

This article originally appeared on HPCwire.

About the SimOps Foundation

The SimOps Foundation is an independent non-profit community organization dedicated to the standardization and automation of simulation operations (SimOps) within the High-Performance Computing (HPC) and engineering simulation sectors. By bridging the gap between engineering simulation and HPC infrastructure, the Foundation provides a vendor-neutral framework for improving the efficiency, scalability, and reproducibility of complex simulations and data flows. Through its tiered certification programs, the “SimOps-compliant” software stack, and a global community of practitioners, the Foundation empowers organizations to accelerate AI-powered innovation and streamline product development. Headquartered in Sunnyvale, California, the SimOps Foundation is built on a decade of expertise and over 200 real-world engineering use cases. For more information, visit www.simops.com. 

Together, they will deliver a simulation platform to manufacturers across automotive, aerospace, electronics, and industrial markets, where surface quality is a performance and compliance requirement.

“This acquisition strengthens our ambition to build the leading simulation platform for electrochemical processes,” says Diego D’Udekem, CEO of Elsyca.

Surface finishing is not just an afterthought at the end of the manufacturing process, according to Elsyca. Across the industries that demand high standards, such as the structural integrity of aerospace components, the electrochemical processes governing coating, plating, anodizing, and corrosion protection are critical engineering disciplines that demand rigorous simulation, Elsyca adds.

Elsyca has spent decades developingCAE tools that allow engineers to model, predict, and optimize electrochemical processes with precision. Its simulation software enables manufacturers to visualize current density distributions, predict coating thickness uniformity, optimize rack and jig designs, and eliminate physical trials, all in a virtual environment before a single part enters the production line.

The combined company will serve a range of high-demand industries, each with surface finishing requirements that benefit from advanced simulation. In automotive, electroplating and e-coating are critical for corrosion protection and the surface integrity of electric vehicle battery components. In aerospace, CAE simulation allows engineers to validate anodizing and conversion coating processes against exacting military and civil aviation standards before qualification testing.

“Every one of our target industries has the same underlying challenge: surface finishing processes are complex, sensitive, and expensive to get wrong,” says Baptiste Fedi, CEO of Hivelix. “Our shared mission is to give engineers the simulation tools they need to get it right the first time, every time, whether they are finishing a turbine blade, a luxury watch case, or an EV battery housing.”

AI-Enhanced Simulation

The combined entity will accelerate the integration of artificial intelligence and machine learning into its CAE simulation platform. 

Digital twin technology will also be a focus, enabling manufacturers to maintain living simulation models of their production lines that update in real time with process data.

“Simulation and AI are not competing approaches, they are deeply complementary,” says d’Udekem. “Physics-based simulation gives you understanding. AI gives you speed. Together, they give manufacturers the ability to engineer surface finishing processes with a higher level of precision and confidence.”

Customers and partners of both Elsyca and Hivelix can expect full continuity of existing products, services, and support throughout the transition. A combined product roadmap, including new integrated simulation modules targeting specific industry workflows, will be shared in the months ahead.

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

"The goal of the COMSOL Innovation Contest is to spotlight the work that our users are doing, that is, how they're using COMSOL Multiphysics to create impact in their own research and product development," says Oscar Littmarck, vice president of marketing for COMSOL. "We hope the winning innovations inspire other researchers and engineers."

Individuals and teams are invited to submit work in one of six categories: (1) aerospace and defense, (2) energy, (3) life sciences and medical technologies, (4) manufacturing and process engineering, (5) semiconductors and consumer electronics, and (6) automotive, transportation, and mobility. One individual or team from each of the six categories will be selected as a finalist.

In addition to the COMSOL Innovation Contest, attendees have two more opportunities to share their work. They can submit an abstract for the chance to:

• Present a poster in the conference exhibition hall
• Have their paper published in the online conference proceedings

Conference attendees will vote for one winner of the Best Poster by Popular Vote award, and the COMSOL Conference program committee will select one winner of the Best Paper award. The poster and paper showcase opportunities are available to those who apply to the contest but are not selected as a finalist, as well as those who do not wish to apply for the contest.

Application Submission

Applications for the COMSOL Innovation Contest are open. Each submission must include a 500-word abstract and a brief video (3 minutes or less) highlighting the simulation work, along with 1–4 images of the models used. 

The deadline to apply for the COMSOL Innovation Contest, as well as for the poster and paper opportunities, is June 5. Those who apply by May 8 and receive approval to showcase their work will qualify for the discounted-registration Early Bird Presenter Rate.

For more details and deadlines, and to see the full submission guidelines, visit: https://www.comsol.com/conference/showcase/boston.

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

In March, GTC returned to San Jose, California, drawing an estimated crowd of 30,000 with vested interest in the growing AI commerce.

Query Your Unstructured Data with GPU Acceleration

In his keynote, NVIDIA CEO Jensen Huang pointed out AI has evolved far beyond perception (powered by computer vision) to generative capabilities, and now to Agentic and ultimately Physical AI. "We are now at the beginning of a new platform shift," he said. "You won't ask [the AI] who, what, where, when, and how questions. You will ask it to create and do." 

The new generation of AI is capable of dealing with the types of unstructured data that will be important for unlocking value in manufacturing applications–video, images, maintenance logs, sensor data, and more. "It represents the vast majority of the world. Vector databases, PDF files, videos, speeches ... Until now, this data has been completely useless to the world, because you cannot easily search or query them. There's no easy way to index them," explained Huang. "But now, we can have AI do that. Just as AI was able to solve multi-modality perception, it can use the same technology to read a PDF."

All the Big Players in CAD and FEA

During GTC, NVIDIA announced it is working with simulation and design ISVs Cadence, Dassault Systèmes, PTC, Siemens, and Synopsys to bring NVIDIA CUDA-X, NVIDIA Omniverse, and GPU-accelerated industrial software and tools to their manufacturing customers. 

"Every single one of these companies needs to compute – lots and lots of it. They need tokens – lots and lots of them," said Huang. "Every company is going to be thinking about their token factory." NVIDIA's proposed solution is the NVIDIA Vera Rubin DSX, described by Huang as "an AI factory platform."

In the design, configuration, and validation of the reference DSX system, NVIDIA used a number of partner technologies, including those from Dassault, Cadence, Siemens, and PTC. In addition, NVIDIA is also collaborating with companies across the MEP value chain to facilitate building massive AI factories, including Trane Technologies, Schneider Electric, Switch, Vertiv, and others to ensure coordination across infrastructure, power, cooling, software and compute. You can read more about those efforts here.

NVIDIA’s technology has been at the heart of ISV efforts to incorporate GPU acceleration into simulation solvers for multiple physics (CAE) and electronic design automation (EDA). Now the companies are working together to incorporate AI into simulation solutions in a number of ways.

Cadence, Dassault Systèmes, Siemens and Synopsys are accelerating engineering workflows by bringing agentic AI into their platforms, using the NVIDIA NeMo platform and Physics NeMo, NVIDIA Nemotron open models, NVIDIA CUDA‑X libraries and NVIDIA accelerated computing to power autonomous design agents for complex chip and system workflows. These solutions include Cadence's ChipStack AI SuperAgent, which combines electronic design automation (EDA) with agentic orchestration; Dassault's partnership with NVIDIA to combine its Virtual Twin technologies with NVIDIA’s AI technology to establish Industry World Models and agentic capabilities on the on the 3DEXPERIENCE platform; Siemens' Fuse EDA AI Agent for semiconductor/PCB design and manufacturing; and Synopsys AgentEngineer for semiconductor and systems design.

Along with hardware partners like Dell Technologies, NVIDIA and its simulation partners are bringing physical AI to bear for more advanced simulation, developing digital twins with NVIDIA Omniverse Libraries to create factory or system-level virtual environments for analyzing design behaviors and running experiments using massive amounts of real-world and synthetic data. 

NVIDIA’s simulation software partners also announced a number of major case study examples leveraging NVIDIA technology with simulation, design, and digital twin solutions, including ANUC, HD Hyundai, Honda, JLR, KION, Mercedes-Benz, MediaTek, PepsiCo, Samsung, SK hynix and TSMC. You can read more about the announcements here.

NVIDIA is also partnering with leading robotics companies to integrate physical AI with robotics systems. FANUC, ABB Robotics, YASKAWA and KUKA are integrating NVIDIA Omniverse libraries and NVIDIA IsaacSim simulation frameworks into their virtual commissioning solutions to develop and validate complex robot applications and production using digital twins. The companies are also integrating NVIDIA Jetson modules into their controllers for real-time AI inference at the edge.

In addition, developers such as FieldAI and Skild AI are building generalized robot brains using NVIDIA Cosmos world models for data generation and Isaac simulation frameworks to quickly train robots to master new tasks using simulation. NVIDIA also announced Isaac Lab 3.0 in early access, which adds multiphysics simulation and improved support for complex, dexterous manipulation.

The Arrival of NVIDIA Vera Rubin

NVIDIA DGX 1, introduced in 2016, was NVIDIA's first computer for deep learning. Back then, the package included eight NVIDIA Pascal GPUs, churning out 170 Teraflops. This year, Huang introduced NVIDIA Vera Rubin, its new supercomputing platform, as "the next frontier of agentic AI."

A rendering of the inside of the Vera Rubin system. Image courtesy of NVIDIA.

According to the announcement, the platform is designed “to operate together as one incredible AI supercomputer, the chips power every phase of AI – from massive-scale pretraining, post-training and test-time scaling to real-time agentic inference."

NVIDIA has just released the Vera Rubin DSX AI Factory reference design, giving hardware partners a guide for building codesigned AI infrastructure. Vera Rubin's launch partners include Dell and other hardware companies. 

The Vera Rubin system is powered by 72 Rubin GPUs paired with 36 NVIDIA Vera CPUs connected with sixth generation NVLink connection. "We created a brand-new CPU for extremely high single-threaded performance, with incredibly high data output, extremely energy-efficient," Huang said. "We never thought we would be selling CPUs standalone. We are now selling a lot of them. For sure, it's going to be a multi-billion-dollar business for us," said Huang. 

Get Your Own Claws

OpenClaw –written by the Austrian developer Peter Steinberger, published in late 2025 – is the latest code to grab the AI community's attention. It was marketed as "AI that actually does things." It is an AI agent framework that can automate tasks and operate autonomously.

A rendering of Huang’s avatar dressed up in NemoClaw, an open source agentic AI creation program. Image courtesy of NVIDIA.

 "I don't know if [Steinberger] realized how successful it would become. It became the most popular open source project in just a few weeks. It exceeded what Linux did in 30 years," noted Huang. 

NVIDIA is releasing NemoClaw, an open source stack that adds privacy and security controls to OpenClaw. "This provides the missing infrastructure layer beneath claws to give them the access they need to be productive, while enforcing policy-based security, network and privacy guardrails," according to the press release. 

OpenClaw can be used in engineering environments for autonomously monitoring simulation or experiments (as well as sending alerts or logging results) or automating 3D modeling tasks. OpenClaw agents can also interact with the models, libraries, and framework provided by the NVIDIA Isaac platform, helping robotics developers automate and orchestrate simulation workflows. You can read more about NVIDIA’s GTC announcements in this blog.

In addition, Dell announced support for NVIDIA NemoClaw and NVIDIA OpenShell, expanding its collaboration with NVIDIA to advance secure, autonomous AI agents that can automate tasks. NVIDIA NemoClaw is an open source reference stack that adds privacy and security controls to OpenClaw. It installs the NVIDIA OpenShell runtime, part of NVIDIA Agent Toolkit, to provide a secure, sandboxed environment for running autonomous agents with no code changes. 

Introducing the Dell Pro Max with GB300

According to the company, the Dell Pro Max models with GB10 and GB300 provide “purpose-built desktop platforms that allow enterprises to build and run autonomous, self-evolving agents locally with frontier-level intelligence.” Dell will be the first OEM to ship a desktop with the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip.

Dell GB10 and GB300 are based on NVIDIA Grace Blackwell architecture…… specs here. They support compute-intensive AI engineering, simulation and local development workflows. The GB300 supports up to 1 trillion parameter models for AI training, inferencing and simulation. Developers can build, run, and optimize new solutions around the clock with autonomous agents with frontier-level intelligence.

For AI Developers, the Dell Pro Max with GB300 provides the means to develop and fine tune physical AI models and incorporate intelligence into robotics systems, as well as other types of autonomous systems like cars and industrial machines. Using computer vision and sensor data, manufacturers can also leverage AI to predict equipment failure and establish preventive maintenance schedules, and to develop digital twins.

At the event, Charlie Walker, Product Manager, Dell Technologies, demonstrated the Dell Pro Max tower with NVIDIA Grace Blackwell Ultra GB300 superchip. “It’s the same chip you would find in a data center, so you can now execute the same agentic AI-powered tasks on premise without going to the cloud,” Walker said. “You can use it for a small office or a work group.”

In a pre-brief before the event, Walker said the new GB10 and GB300-powered computers could be used in a variety of scenarios. “You can take some of the load off the data center and put that work at the deskside,” he said. “We can also envision a smaller company using it as their local LLM host.”

The Dell Pro Max with GB300 has up to 20 petaFLOPS of FP4 performance and 748GB of coherent memory, and enables autonomous agents at trillion-parameter scale. The unit also features Dell’s MaxCool, which can remove heat up to five times more efficiently.

Dell AI Platform with NVIDIA

This year marks the second anniversary of Dell AI Factory with NVIDIA, and the company announced new advancements, including the availability of new AI-ready workstations and server products, as well as the Dell AI Data Platform. The Dell AI Factory combines Dell data center and workstation hardware with software and services to develop and deploy generative AI at scale. Manufacturers can leverage the technology for creating factory-level or system-level digital twins; analysis and simulation of fleets of autonomous vehicles or robots; and for large-scale autonomous design tasks. 

Data management and indexing are significant obstacles when it comes to new AI deployments, so Dell and NVIDIA have collaborated on the Dell AI Data Platform with NVIDIA, which company says can activate enterprise data for AI “while maintaining security, governance, and best-in-class performance at scale.” This can provide significant speedups for vector indexing, data processing and time-to-first-token compared to traditional approaches.

The Dell Data Orchestration Engine automatically discovers, labels and enriches data (structured, unstructured and multimodal) into AI-read data sets at scale. The solution also provides users with a library of pre-built NVIDIA blueprints and NIM microservices, along with the NVIDIA Nemotron 3 Super model on Dell Enterprise Hub on Hugging Face. 

Dell will also support the NVIDIA STX modular reference design. A new AI Assistant in the Dell Data Analytics Engine adds a natural language interface into SQL analytics. Additionally, the introduction of NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs will “bring acceleration directly into the data platform layer,” the company said.

Deskside AI Performance

Dell also announced its new lineup of Pro Precision workstations. The new Pro Precision 9 Towers (available in T2, T4 and T6 configurations) provide deskside AI performance and visualization. 

The Pro Precision 9 T6 extends the T4 architecture and adds a full expansion bay, with up to 15 PCIe slots, support for 2× 600W or up to 5× 300W NVIDIA RTX PRO 6000 Blackwell GPUs, and up to 316TB of maximum storage capacity.

The Dell Pro Precision 9 T6. Image courtesy of Dell Technologies.

The Pro Precision mobile workstations (5 and 7 Series, available in 14-in. Or 16-in. configurations) feature the latest Intel or AMD processors with faster NPUs.

The Dell Pro Max with GB10 can deliver up to 1 petaFLOP of FP4 AI performance in a compact, power-efficient system. It offers 128GB of coherent unified memory, allowing enterprises to run larger models and autonomous agents locally, scale to 4x configurations, and take advantage of the full NVIDIA AI software stack and ecosystem, the company said. 

“Autonomous agents are the next wave of AI, but enterprises won't deploy them unless they can run locally on sensitive data with strong security controls,” said Jeff Clarke, chief operating officer at Dell Technologies. “Our Dell Pro Max desktops and NVIDIA OpenShell help solve that. We're first to ship this capability, and it fundamentally changes how developers build and deploy AI.”

You can read more about Dell’s GTC announcements in this blog.

Though numbers were off from recent AMUGs in the 2020s, the 700-plus guests —largely AM users, educators, and OEMs— hailing from the United States, and other countries, including Europe and Asia—came to the “for users, by user” event with intent to network, learn, be inspired, and engage in hands-on workshops while connecting and reconnecting with industry peers, speakers, educators, and friends.

“AMUG is about relationships first and content and everything else second,” said Ben DiMarco, a presenter of the America Makes Hard Knocks session at this year’s event.

Adds AMUG Secretary Heather Natal of GoEngineer, “You’re around the same people for a week. A bunch of 3D printing nerds in one group together who are really passionate to make sure things are moving forward.”

One of this year’s two main keynotes presented on his passion for making a business case out of AM. Ronen Hadar, Senior Director and Head of Additive Design & Manufacturing for The LEGO Group who attended his first AMUG 12 years agi as an engineer, presented on “Additive Manufacturing at Scale in Consumer Goods: The Case of The LEGO Group.”  

Ronen Hadar presented on “Additive Manufacturing at Scale in Consumer Goods: The Case of The LEGO Group.” Image courtesy: AMUG/Ed Winters

Early in his talk, he tackled the repeatability challenge with AM: “If you know anything about the LEGO brick—and I know most of you are engineers—you know that if you take away the toy part, it is quite an engineering feat. We make tens of billions of those bricks to a ±20 micron accuracy … We are extremely good at making very high-precision parts many times—exactly what AM is not known for,” Hadar noted, half-jokingly.

He later added, “I cannot stress this enough … data as a core competence and accelerator has been the single factor to make it possible for us to produce these quantities in these kinds of tolerances. The reason the product doesn’t fall apart when you build a LEGO set in your homes is because we are fanatics about those tolerances.”

His talk covered the integration of AM in the toy industry, a segment known for high-volume, low-cost production. The LEGO Group, which now has about 100 AM employees worldwide, has co-developed AM technologies, outcomes of which include the release of its first mass-produced retail set that contains a 3D-printed piece.

Injection molding is one of the company’s absolute main core capabilities, which leads to an obvious question, according to Hadar: “Why on earth AM?” Hadar asked.  “Here is where I set the record straight—from our perspective, AM is the magic maker, the stardust that sort of complements everything else we do at the LEGO Group. People who say that AM is here to replace injection molding don’t really know what they’re talking about. It doesn’t need to replace injection molding. It needs to be there to do something that injection molding doesn’t do.”

However, volume is a significant factor when considering use of AM. “Just to set the expectations right, The LEGO group doesn’t do anything below hundreds of thousands,” Hadar said.

The decision whether to go with AM often boils down to cost-benefit analysis: “What’s keeping AM from being implemented in an industry like consumer goods—is the business case of it.

It’s a bit of a non-brainer, also a completely non-emotional decision: Wherever we are cheaper, faster, better, providing more functionality, we will do AM. If we are not, we will not.”

Reflecting on his talk, Shannon VanDeren, owner/president Layered Consulting and current president of AMUG opines that LEGO bricks are “a relatable topic. Very few in the audience have not stepped on or held in their hands a LEGO brick at some point in their lives.” But Hadar’s message extended far beyond child-play memories.

 “[What] he was sharing about the tolerances he is holding in repeatability in billions and billions of LEGO bricks—they are deploying additive extraordinarily and they are achieving tolerances that many of us have not achieved. In fact, I told him, after he came off the stage, your team must have the patience of saints to not become disgruntled and disenchanted at failures because metal additive, first-pass yield, is not always terrific.”

From Grimm’s vantage point, Hadar’s ultimate message was “Don’t count additive out. You can make what seems impossible possible.” Grimm also was wowed by Hadar’s mention of mass production of consumer product tolerances. “That blew me away. I would’ve told you you’re crazy. But it’s possible, and that’s encouraging. That may spur some people to rethink  their applications or how broad that application can be expanded.”

Throughout the event, when not listening to educational tracks, attendees were getting their hands dirty at several popular hands-on workshops including metal casting and materials testing options. Others visited the expo, stopping to mix with dozens of additive experts from companies such as Materialise, Stratasys, GoEngineer, Skuld and more or learning about the latest updates in AM software such as Vixiv’s Vixiv AI or Authentise’s workflow solutions, Cognitive Design Systems’ Cognitive Design or Novineer’s generative design platform. Some visited demo rooms hosted by companies such as HP who demonstrated their new industrial filament 3D printing system along with dozens of AM use cases of their AM product portfolio.

AMUG also hosted another powerhouse keynote earlier in the event: “From Hypercars to Defense Drones: How Two Major Industry Innovators Started Their Partnership Journey at AMUG,” was a collaborative success story between Steve Fournier, of the Additive Designs & Manufacturing Center of Excellence at General Atomics Aeronautical Systems, and Scott Sawyer, Director of Programs at Divergent. The two shared the stage, detailing how an automotive and a defense company discovered transferable additive manufacturing value thanks to their partnership.

“I loved that [this] keynote is a story of finding additive success by basically a collision at AMUG,” said VanDeren. “Two entities who knew nothing of each other until one heard the other on the stage at AMUG in previous years sat down at a coffee shop and just brainstormed. Ultimately now they’re working in unison on very important DoD projects.”

Grimm added, “They repeatedly made the point of telling the audience, ‘Don’t go it alone,’ [advising attendees to] look outside your industry and find those you can connect with, partner with, collaborate with.”

This keynote also revved up DiMarco, technology transition director at the Youngstown, OH-based public-private partnership, America Makes: “I texted one of my colleagues that I’ve been going to additive conferences for 15 years and this is the best I’ve ever been to.”

The presentation involved two people from two different companies who shared the main stage with different but unifying missions, according to DiMarco. General Atomics, an aerospace provider for drones, was wanting to get more rapidly scaled and grow their drone business. The second company, Divergent, had Kevin Czinger present at AMUG  a couple years ago. The company’s initial focus was automotive. “And now they’re making complex components and assemblies for drones and aerospace and defense. Hearing them together on stage sharing similar but different goals and missions was really cool,” DiMarco shared. “Together they brought a powerful message.”

AMUG also played host to various other sessions, including a fireside chat between Grimm and Max Lobovsky, co-founder and CEO of Formlabs, who is the 2026 recipient of the AMUG Innovators Award.

Max Lobovsky, CEO of Formlabs, and Todd Grimm, emcee of the 2026 AMUG have a fireside chat on additive manufacturing. Image courtesy: AMUG/Ed Winters

Lobovsky shared some of his AM insights: “Any good engineer should focus on problems, not solutions. If you focus on the problem -- how do we make a good plastic part quickly and cheaply and you're flexible about the solutions you bring to that problem, you're going to make better products...when talking about engineering you need to understand the problem you're trying to solve.” 

In another session, DiMarco, led a Hard Knocks program on behalf of America Makes. He said what America Makes wants to bring to the AM community is [to share] lessons learned in public formats like AMUG. “Where did they stub their toe and how do you share that with the community so you don’t make the same mistake twice. For us, America Makes is funded heavily from government sponsorship and we want to be really good stewards of that money,” he said.

The event once again hosted a technical competition, announcing winners in Advanced Concepts, Finishing & Post-Processing, and Members Choice. Also the AMUGderby drew dozens of entries and recruited the help of local Boys Scouts Spanish Springs Troop 443.

The 2027 AMUG Conference will take place March 14-18, 2027 at Harrah's Resort Atlantic City in Atlantic City, New Jersey. Interested in speaking at AMUG 2027? Speaker abstract submission opens September 1, 2026. Email trackleader@amug.com with questions.

More Links

America Makes to Host Breakout Session at AMUG

Formlabs CEO Picked for AMUG Innovators Award

AMUG Doles Out Two Scholarships

AMUG Highlights 2nd Keynote Presentation for 2026 Conference

The global 3D Printing Materials market size was valued at USD$4.55 billion in 2025, and total revenues are projected to reach USD$18.32 billion by 2032, growing at a CAGR of 22% from 2026 to 2032.

Key Market Trends & Insights

By Material Type: Polymers dominated the market in 2025, due to their versatility in aerospace, automotive, and healthcare applications. Metal powders and engineered resins are gaining traction, especially in high-performance additive manufacturing for aerospace and defense components.

Metal Powders for Additive Manufacturing: The market for metal 3D printing materials is expanding, especially in aerospace and automotive. Titanium, aluminum, and stainless steel powders are used for producing lightweight, high-strength components. With applications in aircraft engine parts and electric vehicle components, metal powders are poised for significant revenue growth, with expected adoption across North American industries rising by over 35% by 2030.

Industrial Applications: Automotive manufacturers such as Ford and BMW utilize 3D printing materials for prototyping, production of hand tools, and lightweight structural components. 

Geographic Insights: North America remains the largest regional market, with the United States leading adoption of additive manufacturing materials due to industrial R&D investments, government incentives, and a concentration of key 3D printing companies such as 3D Systems, Stratasys, and Markforged. North America accounts for approximately 35% of the global market share, with adoption expected to accelerate across healthcare, aerospace, and defense applications.

Innovations and materials in the pipeline include:

• Protein-based 3D printing materials by 3D Systems & Theradaptive for regenerative therapeutics
• Vega ultra-high-performance filament by Markforged
• Autonomous 3D printing platforms by Ford for prototypes and functional components
• Data Security Platform for additive manufacturing by Stratasys under a $20 million U.S. Navy contract
• Advanced multi-material filaments for medical, aerospace, and automotive applications

Get Full PDF Sample Copy of Report: https://www.maximizemarketresearch.com/request-sample/98121/

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

NeuBird AI will use the capital to accelerate product innovation, scale its global go-to-market efforts and expand accessibility for enterprise DevOps, SRE and IT operations teams, the company reports.

According to the 2026 State of Production Reliability and AI Adoption Report, engineers spend an average of 40% of their time managing incidents rather than building. The top challenges teams face are alert fatigue and insufficient automation leading to engineering burnout, with almost 80% of companies reporting that up to half of their on-call engineers report incident-related burnout symptoms, according to NeuBird AI.

“Enterprise IT, SRE and DevOps teams are under mounting pressure to maintain uptime, simplify incident management and innovate faster,” says Gou Rao, co-founder and CEO of NeuBird AI. “However, modern infrastructure generates an unrelenting flood of alerts, logs and telemetry signals across hybrid and multi-cloud environments, and the tools most teams rely on still require engineers to manually correlate, investigate and remediate incidents. The result is alert fatigue, slower innovation and a disproportionate amount of skilled engineering time lost to troubleshooting.”

NeuBird AI is replacing manual workflows with an autonomous production ops agent that reasons across telemetry sources, correlates signals and delivers root cause analysis (RCA) and remediation in real time. NeuBird AI reportedly acts as an always-on expert engineer with full infrastructure context, applying domain expertise and continuous learning to resolve incidents.

“Every engineer we’ve talked to describes the same problem: they spend the majority of their time responding to incidents rather than building,” says Vinod Jayaraman, co-founder and chief technology officer of NeuBird AI. “This productivity loss is not sustainable. With this funding, we’re focused on putting AI production ops agents into the hands of more enterprise teams, faster and with lower friction than ever before."

NeuBird AI also announced NeuBird AI Falcon, the next generation engine powering its production ops agent. It extends NeuBird AI’s purview beyond incident resolution to deliver end-to-end production operations capabilities.

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

Effective immediately, educational institutions purchasing the Educational Suite and existing education customers on Mastercam CONNECT will receive camInstructor 101 for free. The offering will provide step-by-step lesson plans, project-based instruction, and complete teacher resources alongside Mastercam software.

"Educators have been telling us the same thing for years, they need more than software. They need a teaching solution," says Clint Smith, Mastercam's senior education specialist. "By bundling camInstructor 101 into the Educational Suite, we're giving instructors everything they need to start teaching Mastercam on day one."

What's Included

The camInstructor 101 bundle includes instructional content coveringCNC Mill and Lathe programming packages, with lesson plans, instructional presentations, step-by-step instructions, complete part files, practical tests, and additional teacher resources. Students learn by creating real-world parts in Mastercam.

With worldwide availability slated for future development, the current bundle is initially available to U.S.-based education customers purchasing new Mastercam Educational Suite licenses and to customers currently on Mastercam CONNECT. Current off-maintenance education users who renew their Mastercam CONNECT subscriptions will also receive camInstructor 101 as part of their renewal.

"Our mission has always been to make Mastercam education accessible and effective," says Smith. "This partnership puts a camInstructor developed Mastercam curriculum in front of more educators and students than ever before and that means more graduates entering the workforce with the skills manufacturers need."

Smith continues: "Closing the skills gap isn't just about getting software into classrooms; it's about giving educators the complete support they need to develop the next generation of manufacturing professionals." 

Availability

The Mastercam Educational Suite with camInstructor 101 is available now in the United States. 

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

Developed in collaboration with automotive OEM partners, Keysight Assembly enables engineers to replicate shop-floor processes, including part positioning, clamping, and joining, through guided workflows and templates that require no specialized finite element modeling (FEM) skills. Teams gain visibility into distortion and dimensional risks much earlier than physical prototyping, according to Keysight.

Keysight Assembly integrates with the company's existing stamping simulation software, allowing engineers to carry stamped-part data from forming through assembly and validate outcomes against pre-production scan data. This connects separate stages of manufacturing development into a single workflow.

“Engineers know the frustration of discovering distortion only after parts are on the shop floor," says Mathilde Chabin, director of Product Management for Virtual Manufacturing, Keysight. "Traditional tools stop at part-level analysis and don't reflect how assemblies are actually built. Keysight Assembly simulates real production workflows, so teams can see process sensitivity early, when changes are easy and inexpensive.”

Resource

• Keysight Assembly

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

2026.2 version enables reading of third-party software vendors updates:

• CATIA V5-6R2026
• NX 2512.4000 Series
• Parasolid 38.1
• Autodesk Fusion versions up to 2606.1.36

Features of v. 2026.2

• Reading of all non-default configurations from SOLIDWORKS, in their graphical forms
• Support of SOLIDWORKS Assembly Constraints and Mates
• Support of glTF Metadata (extras) in both reader and writer
• Improved various assembly components from ZW3D files
• SolidEdge reader now available on ARM platforms

Additional Improvements

• General improvement on Metadata/Properties and Layers support
• Continuous consolidation of PMI graphics and semantics
• Enhancement about semantic connections between entities

Performance Updates

• Time optimization for JT export
• Support for Embedded glTF export (binaries directly stored into .glTF files)
• Assembly processing time optimisation for CATIA formats

With Datakit, users should be able to optimize and secure integration at every stage of the software and product lifecycle—from design to production.

All updates are available with end-user products (CrossManager, CLI and plug-ins) as well as solutions dedicated to developers (CrossCad/Ware libraries).

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

In one of the two core scenarios, working with a 3D model, a user uploads a model and assigns a task, and AI Superagent works as a technologist: it develops a machining strategy, determines how to implement it in ENCY, creates operations, adjusts parameters, runs calculations, checks simulation results, and refines the outcome when needed.

“We see AI Superagent not as a black box, but as a new model of collaboration between AI and a CAM programmer..." —Andrei Kharatsidi, CEO of ENCY Software

The second core scenario is working with an existing project. AI Superagent can connect to a new model, and to an active project: continue the work, make changes, help refine the solution, and move forward from the project’s current state.

AI Superagent can also work autonomously from start to finish. But the human does not disappear from the process. At any point, the user can step in, stop the workflow, refine the requirements, adjust the material, machine, surface finish targets, or any other condition. So AI-first CAM workflow does not remove the CAM programmer from the loop—it changes the role of the programmer inside it.

“We see AI Superagent not as a black box, but as a new model of collaboration between AI and a CAM programmer, where AI takes on more and more of the execution, while the human sets the goal, guides the process, and evaluates the result," says Andrei Kharatsidi, CEO of ENCY Software. "Today, we assess the system as operating much closer to an experienced CAM specialist than to a novice. At the same time, the purpose of this closed beta is to understand how close AI Superagent can get to the level of a truly strong CAM programmer in real-world scenarios. In our view, AI-first CAM workflow has the potential to radically change the way people work in CAM.”

The closed beta begins on April 13. Join the wait list here.

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

BricsCAD for General Design

Many improvements in V26.2 focus on core CAD functionality and are available across BricsCAD Pro and BricsCAD Ultimate, which also includes BIM, Mechanical, and Survey workflows.

At the center is BricsCAD Assist, a new AI-driven panel built directly into the interface. It helps users find documentation, how-to guidance, and answers without leaving their drawing, helping reduce context switching. 

With Dynamic Blocks, currently only available outside the United States, users can now create constraint parameters for Dynamic Blocks and manage Dynamic Block properties through Tool Palettes. 

Convert and edit SHX text from PDF imports, which allows imported text to become editable automatically. Notification to improve Drawing Health runs in the background.

QDIM Advanced mode to reduce dimensioning time enables multi-directional chained dimensions in a single workflow, with preview and control built in.

BricsCAD BIM

New integrations with Autodesk Construction Cloud and Bentley ProjectWise simplify staying connected to project environments. 

Inside BricsCAD, faster performance for BIM sections helps on larger models.

The new IFC export dialog box provides visibility into what’s being shared, along with filters and user profiles to control the output precisely.

BricsCAD Mechanical

BricsCAD now connects directly to Siemens Teamcenter and Autodesk Vault integration, bringing PLM and PDM workflows closer to the design environment. 

There are improvements to DWG support: including DWG support for AutoCAD Mechanical 2022 format and improved mechanical DWG stability.

Local sheet metal repair for targeted fixes allows you to fix specific features without reworking an entire part, while associative title blocks and mechanical data make it easier to include mechanical properties directly in annotations.

Explore what’s new in BricsCAD V26.2.

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

The release allows OpenClaw agents to execute 3D generation tasks via application programming interface-based Skill invocation.

Packaging 3D Generation as a Callable Skill

The Skill encapsulates Hitem3D's generation capabilities into a standardized execution flow. Within OpenClaw, agents first verify API credentials, then detect task types—such as single-image, multi-view, batch processing, or portrait generation—before confirming parameters including model version, resolution, output format, and generation mode.

Once configured, the agent submits the generation job via API, polls execution status, and returns downloadable results along with a structured parameter summary. In cases of failure, workflows may include retry guidance, such as adjusting resolution or input quality.

Parameterization for Flexible Use Cases

The Skill exposes a defined capability matrix, allowing developers to control model variants, resolution tiers, output formats such as GLB, OBJ, STL, FBX, and USDZ, as well as generation modes including geometry-only or integrated texturing. This enables the same workflow to support use cases from previews to fabrication-oriented outputs.

At the model level, Hitem3D applies a structure-aware integrated texture generation approach, integrating geometry and texture generation within a unified workflow designed to improve surface consistency and downstream compatibility. This helps reduce visible seams and avoids many common texture-related issues.

Designed for Downstream Usability

The system reduces isolated or unsupported mesh elements, improving downstream usability. Outputs are compatible with common slicing software, where models can be prepared using standard repair tools.

Models may require minor adjustments before printing. In internal tests, most outputs were processed with minimal manual intervention, often using built-in auto-repair features. Wall thickness can be adjusted for typical FDM and resin printing requirements.

Developers can explore the Skill implementation and integrate it into their agent workflows at: https://clawhub.ai/lihuihui-bj/hitem3d.

Hitem3D v2.0 is available worldwide. Explore sample outputs at hitem3d.ai and hitem3d.ai/3dprinting/use-case.

About Hitem3D
Hitem3D, pioneered by Math Magic (founded 2024), converts single or multi-view images into production-ready 3D models for 3D printing, and industrial design. The platform serves users in more than 150 countries and integrates into professional digital production workflows.

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

The Digital Twin Composer integrates digital twin data, simulation and real-time operational signals into managed, high-fidelity industrial environments. By introducing physics-based simulation into real manufacturing and infrastructure contexts, the Digital Twin Composer enables organizations to simulate, validate and optimize changes before physical execution. Availability in India is expected toward the end of calendar year 2026.  

Siemens is also advancing immersive lifecycle visualization through the Teamcenter Visualization Digital Reality Viewer software, powered by NVIDIA Omniverse libraries. The solution enables stakeholders to explore photorealistic digital twins collaboratively. 

“India is at the forefront of industrial transformation, and the combination of digital twins, Industrial AI and advanced computing will help organizations innovate faster and operate more efficiently,” says Mathew Thomas, vice president and managing director for India, Siemens Digital Industries Software. “Our collaboration with NVIDIA brings together powerful technologies that enable customers in India to move from concept to reality with greater speed, accuracy and confidence.”

“Industrial enterprises in India are seeking advanced computing solutions to manage the complexity of modern engineering and scale production through physics-based simulation,” says Vishal Dhupar, managing director, South Asia, NVIDIA. “The integration of NVIDIA Omniverse and accelerated AI infrastructure with Siemens’ Teamcenter Digital Reality Viewer and Digital Twin Composer provides organizations the immersive tools needed to build and optimize high-fidelity digital twins.”

Siemens’ software engineers in India work on core technologies that form the backbone of some of the global product announcements including the Siemens’ Teamcenter Digital Reality Viewer and the Digital Twin Composer. The collaboration reflects how India is an innovation hub shaping the future of industrial AI. 

Together, Siemens and NVIDIA are advancing the Industrial Metaverse by combining industrial software, automation and accelerated computing into an integrated Industrial AI Operating System. With the partnership offerings now available for Indian customers, organizations can access immersive engineering and simulation capabilities that connect the real and digital worlds.

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

"For years, AI in engineering was viewed primarily as potential," said David Heiny, CEO and co-founder of SimScale. "What we're seeing now is a shift from experimentation to scaled execution. Compared with our 2025 report, the share of organizations actively experimenting with pilots and running mature, scaled AI programs has nearly doubled, signaling a clear wave of adoption. The teams pulling ahead are not just adopting AI tools, they're embedding AI into real engineering workflows built on cloud-native platforms, expanding the design space in ways that simply aren't possible with legacy infrastructure. That's when you move beyond incremental efficiency gains and start seeing measurable impact on both innovation and commercial performance.”

The report findings point to several defining themes shaping how engineering organizations are adopting, scaling, and operationalizing AI today:

• 75% of organizations with mature AI programs cite cloud-native infrastructure as a key enabler.

• 70% of respondents cite secure data governance and access controls as critical enablers of scaling AI initiatives.

According to SimScale, the data reinforces the idea that scale is less dependent on perfect data architecture and more closely associated with infrastructure readiness and governance maturity. The broader opportunity hinges on modernizing infrastructure so AI can deliver value within real engineering processes now, while data foundations continue to mature.

The survey also found that 92% of respondents reported deploying surrogate models to accelerate simulatoin or design exploration in 2025. However, they are still using those models in a limited way, with just 8% saying those models are extensively deployed in their workflows.

Image courtesy of SimScale.

The survey also found that:

• Organizations using AI-enabled workflows reported an average 2.8x speedup in servicing simulation requests compared to conventional workflows.

• Organizations using AI-enabled workflows report request for quote (RFQ) and technical bid turnaround times approximately three times faster than those using conventional workflows

• Accelerating simulation turnaround and RFQ response times helps organizations respond to customer requirements faster and compete more effectively in time-sensitive markets.

• Across every major stage of product development, AI copilots are significantly more common than autonomous agents, with adoption ranging from 67% in requirements engineering to 76% in simulation/computer-aided engineering (CAE), while only about 10% reported deployment of fully autonomous AI agents.

• 87% of organizations have the necessary governance in place for AI to take autonomous pass/fail decisions at design gates, with 8% routinely adopting this practice. This trend indicates a widespread expectation of increasing autonomy of AI agents.

According to the report, the data reveals that organizations are progressing deliberately when it comes to full autonomy. Copilots are becoming embedded in day-to-day engineering tasks, assisting with simulation setup, analysis, and workflow coordination. However, fully autonomous agents remain limited in deployment, reflecting the high-consequence nature of engineering decisions. 

The report also looked at the impact of AI on design space exploration in different verticals:

• In Machinery and Industrial Equipment teams, 88% of organizations using AI workflows iterate daily or multiple times per day, compared with just 12% using conventional workflows.

• In Life Sciences and Healthcare teams, 64% of organizations using AI workflows iterate daily, while conventional workflows show virtually no daily iteration.

Across industries, the survey results show that AI-enabled workflows are significantly changing how quickly engineering teams can explore and refine designs. Instead of running simulations only periodically due to time and compute constraints, many teams can now iterate on designs daily or even multiple times per day. This faster pace of experimentation allows engineers to test more configurations, identify potential issues earlier, and converge on optimized solutions more quickly, expanding the practical design space within a single development cycle, the company said.

The full 2026 State of Engineering AI Report, including detailed findings and analysis, is available here. 

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

"The U.S. Government has made it clear that to advance our defense readiness we cannot rely on geopolitically sensitive regions for the materials essential to our most advanced weapon systems," says Frank Roberts, CEO of 6K Additive. "By upcycling domestic scrap from DoD stockpiles and maintenance centers, we are creating a circular, secure, and sustainable supply chain for the US defense sector. This Award enables us and the DoD to further identify end-of-life parts and scrap to convert back into high-value powder ultimately leading to strategic components for the military."

From Scrap to Frontline

The goal of the Award is to maximize use for end-of-life components, high value metal alloys by leveraging the baseline processes developed in previous 6K Additive DLA awards. Specifically, the Scope of Work (SOW) for Phase III is expected to focus on materials from DLA Disposition Services, the Navy and the Air Force Life Cycle Management Center and aligning processes to maximize value for the DoD. The scope of work for the Award includes:

• Identify and collect material from DLA Depots, to primarily use DoD scrap as a source for domestic critical metals.
• Proof of concept for robotic system for the automation of scrap identification and subsequent sorting.
• Convert Nickel, Titanium, Tungsten, and C103 (Niobium alloy) end of life parts into high-value powder.
• Conduct cold spray trials to investigate the mechanical properties of upcycled Nickel and Titanium powder for use as a repair technology

Partnership with the US DoD

6K Additive will collaborate with the DLA and other agencies within the US DoD to obtain high-value scrap from these centers and depots.

Specific operations will take place at major aviation depots, which generate upwards of 60,000 pounds of mixed scrap metal weekly. Upcycled powders will be returned to the military for rigorous testing against virgin metal standards to ensure performance in critical defense applications.

Project Timeline and Impact

The Award has an 18-month timeline, and the program aims to prove the concept of an automated sorting prototype and deliver certified batches of tungsten, titanium, and niobium powders. 

About 6K Additive

6K Additive, Inc. (ASX: 6KA) is a US-based manufacturer and tsupplier of premium metal powders for additive manufacturing and alloy additions for the aluminum melt industry, all made from sustainable sources. The company is headquartered in Burgettstown, PA.

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

“As ROSEN celebrates 45 years of innovation, this award emphasizes our commitment to supporting bold ideas that challenge convention and create measurable impact in pipeline safety and integrity. We look forward to receiving contributions that reflect the same spirit of purpose and ambition that has guided ROSEN since its founding and continues to guide us also into the future,” said ROSEN’s Chief Executive Officer Andreas Opfermann.

Founded in 1981, ROSEN Group is a consultancy and specializes in inspection of industrial equipment, among other services. 

“Following a successful inaugural year, the ASME Foundation is proud to continue our partnership with ROSEN Group to support the next generation of pipeline innovators,” said Stephanie Viola, executive director of the ASME Foundation and managing director of ASME Philanthropy and Programs. “The Hermann Rosen Award reflects our shared commitment to empowering engineers and students whose ideas can strengthen infrastructure and advance the industry.”

Full program details, eligibility information, and submission guidelines are available at the ASME Foundation website.

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

The collaboration draws on AVEVA’s portfolio, including the CONNECT industrial intelligence platform and industrial digital twin capabilities. It is projected to advance time to token for AI Factories, using domain-specific simulations, digital visualization and collaborative design tools to maximize GPU efficiency and accelerate deployment of AI Factories at speed and scale, AVEVA reports.

AVEVA is incorporating its solutions into the Omniverse DSX Blueprint, bringing benefits of digital twins to every stage of the AI factory lifecycle:

• Customers may bring OpenUSD SimReady assets into AVEVA Unified Engineering through a new converter, enabling them to reuse existing assets, design new ones, and leverage high-fidelity SimReady data and environments built on NVIDIA Omniverse libraries.
• With a single source of truth from AVEVA Asset Information Management, customers can manage equipment, systems and make changes.
• With AVEVA Process Simulation, customers can model and run simulations of advanced liquid-cooling networks for AI Factories to refine designs.
• AVEVA’s PI System enables customers to aggregate IT and OT data across NVIDIA Omniverse DSX Exchange.
• Last, customers can use AVEVA Operations Control and Unified Operations Center to manage data center infrastructure, which comprises of electrical (UPS, switchgear, PDU, generator etc.), mechanical (chiller, CDU etc.) and safety systems into one scalable unified platform using a templatised situational awareness approach. 

"AI Factories are fast becoming the industrial-scale engines of the global digital economy," says Rob McGreevy, chief product officer, AVEVA said.  "To drive this transformation, AVEVA and NVIDIA are creating a new approach to digital twin deployments, founded on domain-specific expertise, pioneering software and operational excellence. Together, our companies are creating this new digital twin at scale, combining SimReady assets, NVIDIA hardware, and IT and OT data-driven insights to design, build and AI-optimise the intelligent industries of the future.”

“The rapid rise of gigawatt-scale AI Factories requires a new class of industrial intelligence to optimize the entire lifecycle of these massive data centers, from initial design to real-time operations,” says Vladimir Troy, vice president, AI Infrastructure, at NVIDIA. “By integrating AVEVA’s engineering and simulation software into the NVIDIA Omniverse DSX blueprint, we are providing developers with a unified digital twin architecture to accelerate the deployment and efficiency of the world’s most advanced AI infrastructure.”

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

The Dell Pro notebook releases are available with the latest Intel Core Ultra Series 3 and AMD Ryzen AI 400 processor options, and a modular design that shrinks the motherboard to create room for larger fans and improved thermals, provides more energy in smaller batteries and supports a wider range of silicon options within the same chassis, the company says. Additional Intel Core Series 3 systems will be introduced at a later date.

The new notebooks include 14-inch Dell Pro Premium, which is up to 7% thinner than the previous generation; the Dell Pro 7 in 13 and 14-inch sizes (18% thinner than prior releases); the Dell Pro 5, available in 14- and 16-inch sizes; and the Dell Pro 3 in 14- and 16-inch sizes.

The Dell Pro 5 Micro is an ultra-compact computer with integrated Type-C connectivity up to 100W power delivery-in, which can be powered directly from a USB-C monitor, such as a Dell Pro P Monitor, minimizing cord clutter. According to the press release: "As Dell’s first Copilot+ PC mainstream desktop, it delivers 50 TOPS NPU for low-power acceleration of AI workloads and supports memory speeds up to 7200 MT/s for smoother multitasking."

The Dell Pro Precision 5S workstatoin is the entry-level model in the line, and the company says it can handle light CAD modeling, video editing and graphic design tasks. It is available with the Intel Core Ultra Series 3 featuring integrated Intel Arc Pro graphics with up to 12 Xe or AMD Ryzen AI 400 processors with AMD Radeon PRO graphics.

You can read about Dell's earlier workstation announcements at NVIDIA GTC here.

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

Experts in the industry weigh in on how the so-called hybrid manufacturing approach can positively impact production strategies now and in the future, and whether there’s a need to even view it as “hybrid” manufacturing or just simply matured industrial manufacturing.

DE: How does your company use or view additive manufacturing as part of a hybrid manufacturing process? 

DE: How does your company use or view additive manufacturing as part of a hybrid manufacturing process? 

Eric Utley, Protolabs 3D Printing Applications Engineering Manager: For us, combining additive and subtractive manufacturing on a single component is very common. The part will be printed to ~95% near net shape and we will use secondary machining for the critical features. These are typically features and surfaces that need either a tighter tolerance or a smoother finish as-printed, or both.

Gert Brabants, Business Line Manager Series Manufacturing, Materialise Manufacturing: AM is not merely a replacement, because it has its own unique benefits, such as complex designs, small-batch production, fast cycle times, etc. AM excels at complex geometries, lightweight structures, custom parts, or small-batch runs; while subtractive or traditional methods may be better for simple, high-volume parts. If you don’t make use of these benefits, you’re indeed just doing a straightforward replacement of traditional manufacturing. And your business case will (almost) always fail.

The real value of hybrid manufacturing lies in combining the strengths of both approaches. AM offers efficiencies in terms of material use and enabling complex geometries compared to traditional machining. Traditional methods like CNC machining can then deliver the precision where it’s needed. When you combine these strengths in one product, the result is greater than the sum of its parts.

Take jet engines, for example, where complex design optimizations are essential. Or consider drainage parts or air ducting, where we can replace aluminum by polymer, for weight saving, cost savings, and part integration. In semiconductor manufacturing, weight saving and complex geometries are equally critical.

Andre Wagner, CEO, Authentise: At Authentise, we see hybrid manufacturing as starting well before anything is printed.

The DFAM [design for additive manufacturing] and engineering steps such as support strategy, orientation, nesting, and toolpath generation are not ‘prep work.’ They are first-class steps in the manufacturing flow and just as critical as printing, machining, or finishing. Treating them separately is one of the reasons hybrid environments break down.
Our digital thread is designed to enable end-to-end, in-process control across both digital and physical steps. That includes using real manufacturing data to influence downstream operations. For example, in a project with Holdson, printer sensor data is used to dynamically adjust parameters for a post-processing surface-finishing step. The goal is a single, connected workflow rather than isolated optimizations.

In that sense, additive manufacturing is not a discrete stage but part of a continuous, adaptive process that spans design intent, execution, and post-processing.

DE: What tools does your company view as most successful when used in combination as part of a hybrid manufacturing process?

Brabants, Materialise: We’ve seen the greatest success when additive manufacturing is paired with precision CNC machining and thorough inspection processes. For example, we’ll produce complex, lightweight structures via metal AM, then use CNC milling to finish critical interfaces. NDT [nondestructive testing] tools, such as CT [computed tomography] scanning, help verify internal integrity before assembly. This hybrid workflow ensures we get the design freedom of AM while maintaining the repeatability and precision that traditional methods are known for.

Within our own company, we have access to traditional manufacturing capabilities alongside our AM operations, which allows us to manage this hybrid workflow end to end. But the most powerful tools are still related to design optimization and simulation. They are what unlock the full potential of combining these manufacturing methods in the first place.

Wagner, Authentise: The most successful hybrid environments combine three layers:

1. Design and intent capture: CAD and simulation tools that explicitly define which features are additively produced and which are finished conventionally; and early visibility into downstream constraints like machining access, inspection requirements, and certification.

2. Manufacturing execution and orchestration: MES-style workflow tools that coordinate AM builds, post-processing, machining, inspection, and rework as a single process, not disconnected steps; and scheduling and routing that can adapt as real data comes back from machines.

3. Data, traceability, and feedback: Unified digital thread across AM and non-AM steps, including material history, environmental data, machine parameters, and inspection results; and closed-loop feedback so lessons from machining or inspection inform future designs and build strategies.

The key is not any single tool, but how well data flows between them without manual rework.

Protolabs combines additive and subtractive manufacturing, using secondary machining for critical features. Image courtesy: Protolabs

Utley, Protolabs: Most common is combining additive and subtractive manufacturing, particularly with metal components. Less common is combining injection molding with additive or CNC machining, such as overmolding over a 3D printed substrate or custom CNC machined substrate.

DE: What are the impacts of hybrid manufacturing on product design?

Wagner, Authentise: Hybrid manufacturing fundamentally changes how design intent should be expressed.

Today, CAD models tend to lock a part into a specific machine type, material, and process path. That rigidity works against hybrid environments, where flexibility across machines, materials, and processes is exactly where the value lies. As new equipment and processes emerge, those locked designs become a bottleneck to adoption.

We believe hybrid manufacturing works best when generative design algorithms are directly connected to intent rather than fixed geometry. Instead of designing for one process, engineers define functional requirements, constraints, and priorities, and allow the manufacturing system to resolve the optimal combination of additive and conventional steps.

The impact is designs that are more future-proof, easier to adapt to new machines or materials, and better aligned with real manufacturing capabilities as they evolve, rather than freezing decisions too early.

Utley, Protolabs: Hybrid manufactured parts are typically high-value parts, and the hybrid manufacturing is done because it is required or adds a lot of value. Otherwise it is cheaper to keep to a single manufacturing process. It is very common for aerospace components. Using additive, the part can be made with minimal material to keep weight down, but then secondary machined to get the necessary tolerances and surface finish for assembly. Typically a good rule of thumb is where the part touches other parts in the assembly is where secondary machining will be performed. Without hybrid manufacturing, these components would typically be heavier and made of several machined or cast components that are welded together.

Brabants, Materialise: Hybrid manufacturing changes how we design products by expanding what’s possible geometrically while maintaining the precision and scalability of traditional methods. 

Designers now work with dual constraints: optimizing the portion of the product for additive to leverage complexity and part consolidation, while ensuring conventionally produced features are machinable and meet tight tolerances. We see greater functional integration and smarter tolerance strategies.

Take automotive heat exchangers as an example: AM enables highly intricate internal channel geometries that would be impossible to produce through casting or machining alone, while traditional finishing ensures the mating surfaces meet the precision required for assembly and sealing.

DE: How can hybrid manufacturing advance engineering workflows going forward?

Utley, Protolabs: Hybrid manufacturing should be considered where complexity brings value to a component and precision is required for key features. It is not a great fit for low-value or simple components that can likely be manufactured more cost effectively via a single manufacturing process. It can enable the consolidation of assemblies by printing the main mass and using secondary machining to improve tolerances where it hooks up to the rest of the assembly.

Brabants, Materialise: It’s worth challenging the term ‘hybrid manufacturing’ itself. From an engineering workflow perspective, adding an AM step to a production process is no different from adding any other manufacturing step. If you do metal casting followed by CNC machining, nobody calls it hybrid. It’s simply manufacturing. The same should apply when an AM step is part of the flow. Calling it ‘hybrid’ creates an artificial separation that can hold us back from treating AM as what it truly is: another mature, industrial manufacturing method.

We’ve seen this kind of language evolution before in our industry. Rapid prototyping became prototyping, 3D printing became additive manufacturing, and now industrial manufacturing. The shift from prototyping to production is a sign of maturity. The next step is to drop the hybrid label entirely and simply integrate AM into the broader manufacturing ecosystem.

What will truly advance manufacturing is not redefining how we label these processes, but building integrated management platforms that take the complexity out of coordinating different manufacturing methods and post-processing services. When AM workflows can seamlessly plug into existing production systems, tapping into the same planning, quality, and logistics infrastructure, the distinction between additive and traditional becomes irrelevant. That ecosystem integration is where the real progress lies.

Wagner, Authentise: Hybrid manufacturing enables workflows that are more adaptive, data-driven, and resilient.

Looking forward, we see:

• Faster iteration, because AM allows rapid changes while conventional processes lock in repeatability
• Better use of automation, since workflows are defined once and executed consistently across multiple processes
• Stronger feedback loops, where manufacturing and quality data directly shape future designs

Longer term, hybrid manufacturing is a foundation for agent-assisted and autonomous engineering workflows, where decisions about process selection, routing, and trade-offs are increasingly informed by real operational data rather than assumptions.

In sharing some closing thoughts, Brabants of Materialise adds: “The industry is maturing, and our perspective is that the sooner we stop treating additive manufacturing as something separate or special, the sooner we unlock its full industrial potential. It’s not about additive versus traditional but about choosing the right manufacturing method for each part of the product, managing it all within one connected ecosystem, and delivering the best result for the customer.”

The aim is to support the industrialization and commercialization of space products and services, enabling startups to scale up globally while strengthening Europe’s position in the international space economy and fostering sustainable growth for the sector, according to Siemens.

Siemens will provide industrial-grade digital twin capabilities and a digital engineering and simulation backbone for the European space industry along with access to mentors and experts in the field via an Incubator Program Offer for the EPIC program’s startups. With Siemens Xcelerator, space tech startups will be able to design, simulate and validate complex systems in a virtual environment.
 
The ESA BICs operate across 37 centers in Europe and represent a largenetwork of incubators supporting space-related startups in Europe with the goal of supporting entrepreneurs and helping them to scale. To date, over 2,000 startups have benefited from the program. The Siemens offer will also be available to projects supported by the ESA Technology Brokers and ESA Phi-LabNET.

“As a leading provider of software for space initiatives, supporting emerging startups through our collaboration with the ESA is part of Siemens’ DNA. Europe does not lack ideas in space. It needs runways to take off at scale,” says Cedrik Neike, CEO of Digital Industries and Member of the Managing Board of Siemens AG. “Together with ESA, we are shaping the future of spaceflight: we help startups scale faster and bring their technologies into industrial use.”

“We welcome Siemens’ participation in the EPIC initiative and look forward to the value their technology and expertise will bring to our startup ecosystem,” said Geraldine Naja, director of Commercialisation, Industry and Competitiveness, European Space Agency. “This collaboration aligns with our mission to foster innovation and support the growth of high-potential startups across Europe.”

For more on Siemens cooperation with ESA’s initiative, visit https://blogs.sw.siemens.com/simcenter/siemens-simulation-esa-space-startups/

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

This announcement reflects BMW’s move from fragmented legacy requirements management systems to a single unified data model within Codebeamer.

The solution serves as an enterprise-wide solution for requirements management for the automotive group.

With one shared data model, Codebeamer offers consistent processes, traceability, and digital continuity across mechanical, electrical, and software disciplines, according to PTC. This unified environment provides the scalable foundation needed to enable integrated mechatronics development, collaboration with suppliers, and the adoption of BMW Group’s AI-enabled engineering workflows.

“BMW is demonstrating what true digital engineering leadership looks like,” says Robert Dahdah, chief revenue officer at PTC. “Centralizing requirements management on Codebeamer establishes a robust data foundation for integrated mechatronics and AI-driven engineering, supporting the future of automotive innovation.”

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