Nothing could be further from the truth.

Granted, any new trend will always have an element of bandwagon business from marginal players and hype-riders looking to benefit from the latest trends.

But in terms of how digital transformation and AI are being researched and implemented in the manufacturing industry, there is plenty of steak to go along with all that sizzle.

One of the best ways to distinguish between an over-hyped trend and something with substance is to watch who is watching it. A great place to see that in action was at the recent ARC Industry Leadership Forum, which took place in Orlando, Fla. February 10-13.

This year’s event was almost entirely focused on AI, digital transformation and Industry 5.0 in manufacturing. It attracted more than 600 attendees representing some of the biggest companies in the manufacturing sector.

Indeed, the top 30 of these attending companies with publicly available financial numbers had a combined 2023 market cap of $4.22 trillion. If this market cap were a country, it would rank as the 4th largest economy in the world, just behind Germany ($4.5 trillion GDP) and ahead of Japan ($4.20 trillion GDP). Most of these companies were users undergoing significant digital transformation initiatives.

The fact that these industrial heavyweights are already fully invested in implementing AI and digital strategies shows the scale of the opportunity, and the huge strategic risk of ignoring it—we’re talking Blockbuster Video-level strategic risk.

But the question remains: where do you begin, especially if you don’t have the capital and assets of these massive multinational businesses?

Everywhere, all at once

In the current state of things, engineering leaders can be easily overwhelmed with all the trends and challenges thrown at them. Mathias Oppelt, vice-president of customer-driven innovation at Siemens Digital Industries (Siemens is certainly a technology vendor, but also manufactures its products using the latest smart manufacturing principles), hears about this from his customers daily and summed it up nicely during his session at the ARC Forum:

“You need to act more sustainably; you need to have higher transparency across your value chain. Have you thought about your workforce transformation yet? There’s a lot of people retiring in the next couple of years and there’s not many people coming back into the into the workforce. You still must deal with cost efficiency and all the productivity measures, while also driving energy efficiency. And don’t forget about your competition—they will still be there. And then there’s all that new technology coming up, artificial intelligence, large language models, ChatGPT—and on it goes, all of that all at once.”

Sound familiar?

Even with all these challenges, everything must now be done at speed. “Speed and adaptability will be the key drivers to continuing success. You need to adapt to all these challenges, which are continuously coming at you faster. If you’re standing still, you’re almost moving backwards.” Oppelt said.

The answer is simple, offered Oppelt with a wry smile: just digitally transform. The crowd, sensing his sarcasm, responded with nervous laughter. It was funny, but everyone understood it was also scary, because no one really knows where to start.

Bite the bullet, but take small bites

“The continuous improvement engineers out there know how risky it can be to bite off more than the organization can chew or to try to drive more change than it can manage,” says Doug Warren, senior vice president of the Monitoring and Control business for Aveva, a major industrial software developer based in Cambridge, U.K.

“It helps to take bite-sized pieces, and maybe even use the first bite to drive some incremental benefit or revenue to fund the next bite and then the next bite. You can sort of see this this self funding approach emerge, assuming the business objectives and the metrics tied to those business objectives show results.”

Warren is puzzled by how slowly and conservatively a number of industrial segments have been to fully embrace digitalization and digital transformation, saying that “…it seems like everyone has at least dipped a toe or a foot into the water,” but the number of organizations that are doing it at scale across the whole enterprise is lower than most people would guess.

“The level of technological advancement doesn’t come as a big surprise, and where we go from here won’t be a big surprise. The trick will be how fast you get past the proof-of-concept and into full scale deployment,” he says.

From Warren’s perspective, if you’re not taking advantage of the digitalization process to fundamentally change the way you’re doing work, then you’re probably not getting as much value.

“To just digitize isn’t enough. How do we change those work process? How do we inject more efficiency into work processes to take advantage of the technological advancements you are already investing in? That’s the special sauce,” he says, conceding that it’s difficult because people typically prefer routine and structure. “That’s probably got a lot to do with the lack of real speed of adoption, because you still have to overcome the way you’ve always done it.”

Warren says a good way to look at it is like a more nuanced version of the standard continuous improvement initiatives companies have been undertaking for decades.

“Continuous improvement is incremental changes over time, where digital transformation provides at least an impetus for more of a step change in the way we perform work, whatever that work might be.”

What’s old is new again

One of the main points of hesitation towards full scale implementation of digital transformation or AI initiatives is the perceived ‘newness’ of it and the uncertainty or risk associated with the perception of so-called “bleeding edge” technology.

The thing is, none of this is all that new. The concept of the neural network was developed in the 1940s and Alan Turing introduced his influential Turing Test in 1950. The first AI programs were developed in the early 1960s. If you are a chess enthusiast, you’ve certainly played against AI opponents for the last 20 years. Most popular video games have had story lines fuelled by AI-powered non-player-characters (NPCs) for almost as long.

What has changed over the last few decades is the amount of computing power available, the democratized access to that compute power through the cloud, and the speed provided by the latest advances in chips.

This growth of available computational power and technology can now be applied to all the improvements organizations have been trying to achieve with continuous improvement. And they are proving to be most effective when combined with the extensive knowledge found within companies.

“Industry definitely provides complexities because it’s not just AI and machine learning (ML). There’s also domain knowledge, so it’s really a hybrid approach,” says Claudia Chandra, chief product officer for Honeywell Connected Industrials based in San Francisco.

Chandra earned a Ph.D. in artificial intelligence and software engineering from UC Berkeley 25 years ago and has spent her career working with data, AI, edge platforms and analytics.

“I’m not for just AI/ML on its own. It’s really the domain knowledge that needs to be incorporated along with (AI’s) first principles. The accuracy would not be there without that combination, because data alone doesn’t won’t get you there,” Chandra said.

“That the tribal knowledge needs to be codified, because that gets you there faster and might complement what’s in the data. So, digitization is the precursor to AI/ML—you need to collect the data first in order to get to AI/ML,” she says, reiterating that it must be a step-by-step process to reduce risk.

Chandra says companies that have taken these incremental steps towards digitalization and embrace the cloud or even more advanced tech such as AI/ML will find that their digital transformation is no longer a behemoth with all the pain and risk that go with it. Plus, any vendor with a good understanding of the technology will provide at least a starting point—including pre trained models—so companies don’t have to start from scratch. “But ultimately, as you train it more, as you use it more, it will get better with the data that’s specific to your company,” she says.

Certainly, the success of any AI-enabled digital transformation initiative is all about the underlying data and training the AI appropriately to get the required accuracy. But it takes several steps to set the conditions for value generation: Commit to a project; start small with the right use case; and be persistent and diligent with the data. Once you get a small victory, put the value and the experience towards the next project. With such an approach, you will soon learn why AI and Industry 5.0 are here to stay—and so will your competition.

The post AI and Industry 5.0 are definitely not hype appeared first on Engineering.com.

This episode is brought to you by IP.com. Please complete the registration form to watch the full conversation.

The nature of intellectual property is changing. For designers, scientists and engineers, innovation can have widely different meanings, from a novel way to combine carbon atoms into a molecule for the pharmaceutical industry, to a control algorithm for an interplanetary spacecraft. Once, the concepts of novelty and innovation were simply defined in legal terms, and the methods for protecting innovation were clearly defined.

Today, it’s more complicated. Not only is the concept of innovation more nuanced, but the very nature of information is also in play. How much of a human genome is patentable? How much of a copywritten algorithm is defensible? Can artificial intelligence be assigned a patent? The stakes and never been higher, and the difficulties in keeping proprietary information away from competitors, have never been more challenging.

Joining engineering.com on this episode of The Engineering Roundtable are four experts to discuss this complex topic.

Panelists:

Jim Durkin, Managing Director, Product Management, IP.com
Ameet Bhattacharya, CTO, IP.com
Joe Manico, Research Scientist, Kodak Moments
Elle Gahl, President & CEO, Shadow Ridge Analytics

Moderator:

Jim Anderton, Multimedia Content Director, engineering.com

* * * 

To learn more about IP.com and their AI solutions for innovation, contact sales@ip.com or visit https://ip.com.

The post IP is Changing in the Age of AI  appeared first on Engineering.com.

The company says its new system automatically or semi-automatically analyzes potential process hazards and validates protection mechanisms in an industrial process. Users can then work to prevent hazards using an analysis tool to help identify protective mechanisms to the process.

This patent is a part of Schneider’s strategic initiative to enhance functional safety using AI. It is now possible to simulate hazards with varying conditions and then attempt to prevent dangerous conditions by using a process hazard analysis tool to generate protective actions.

As more industries embrace digital transformation and generate high-quality data, the advantages of implementing AI in day-to-day operations increases. This latest patent from Schneider’s EcoStruxure Triconex Safety team has the potential to identify hazards and safeguards in a process.

Process safety management can then take advantage of industrial, real-time data to revalidate hazard and operability (HAZOP) studies to prevent industrial hazards and save lives.

“We are the first to push this boundary of automating the hazard process analysis with artificial intelligence,” said Chris Stogner, Schneider Electric’s senior director of offer management. “Bringing AI to functional safety has the potential to create a more rigorous and robust HAZOP study, generating more combinations of scenarios and deviations then what was humanly possible before.”

Three other Schneider Electric patents incorporating AI into functional safety lifecycle are currently pending. The company is developing these initiatives to answer a growing interest in combining human ingenuity in functional safety analysis with strategic implementation of reenforced learning to prevent hazardous scenarios in industrial automation.

The post Schneider announces new AI patent for process safety appeared first on Engineering.com.

For starters, Onshape users will be excited to learn that Onshape AI Advisor is coming soon.

The product support chatbot was announced with little fanfare last September, but we haven’t heard much since then. Last week I got an update from Onshape founder and PTC chief evangelist Jon Hirschtick.

“Very soon you’re going to see us launch our Onshape AI advisor. It’s working internally in testing, and it’s going to be a vehicle for lots of cool AI features,” Hirschtick told me.

AI Advisor will provide conversational support, answering questions about Onshape features and best practices. It’s based on a commercial foundation model, but it’s trained on Onshape documentation, so Hirschtick says it will give better answers than a general chatbot like ChatGPT. Each answer will also include links to sources so users can dig deeper if necessary.

I recently described AI chatbots as the “Hello World” of AI applications—the first step, the low-hanging fruit, the “my boss said we need AI so here’s the quickest thing we can do.” That’s not a knock on Onshape AI Advisor; it may prove to be a handy tool, but I think we can all agree that AI’s potential in CAD software is much higher than a chatbot.

Hirschtick sees that potential. He described AI Advisor as “just phase one” of Onshape’s plans for AI and explained how it could evolve to directly support users, such as by writing code or even modifying geometry. Hirschtick also told me the Onshape team is exploring other AI features, including AI-based rendering and generative text-to-CAD.

You can read all the details in Onshape AI Advisor is coming soon—here’s everything we know.

Siemens partners for generative AI in ship design

Speaking of AI’s potential for design software, Siemens announced that it will collaborate with Compute Maritime to “push the boundaries of generative AI in the ship design industry.”

The collaboration will connect Siemens’ Simcenter STAR-CCM+ to NeuralShipper, Compute Maritime’s vessel design and optimization platform. NeuralShipper, which Siemens describes as “a digital naval architect,” quickly generates a fleet’s worth of vessel design options to serve as a starting point for engineering teams. Compute Maritime says the generative AI tool is trained on more than 100,000 designs spanning a wide variety of vessel types.

By connecting Simcenter STAR-CCM+ to NeuralShipper, Siemens says it will bring computational fluid dynamics (CFD) and results validation to the ship design software. “The combination… enables the creation of novel vessel types and demonstrates how designers can automate simulation processes and predict real-world performance, even for the most unconventional designs,” Dmitry Ponkratov, Siemens’ marine director for simulation and test solutions, said in the press release.

However this collaboration pans out, it certainly won’t be the first time ship designers use Siemens software. You can read about another example in Design software helping to build the largest cruise ships.

Bentley opens infrastructure award nominations

Are you, or do you know, an infrastructure project worthy of recognition?

Nominations are now open for Bentley Systems’ 2025 Going Digital Awards, an annual program honoring infrastructure around the globe. Spanning 12 categories including bridges and tunnels, rail and transit, structural engineering, and more, the Going Digital Awards will be decided by independent jurors and announced on October 15, 2025 at Bentley’s Year in Infrastructure conference in Amsterdam.

You can submit your nominations here before March 31, 2025.

Quick hits

• Users of Concept NREC’s AxCent platform for turbomachinery component design can now access Ansys’ CFX solver to evaluate machine performance within the AxCent interface.
• As part of its collaboration with TSMC, Siemens announced a certified automated design workflow for TSMC’s InFO packaging technology.
• VI-Grade launched the Compact HMI Simulator, its latest real-time driving simulator for developing and testing human machine interfaces (HMIs).

One last link

Model-based definition (one of the many answers to the question No really, what is MBD?) is an alternative to 2D drawings that aims to imbue 3D models with manufacturing information. It’s an intriguing idea, but it hasn’t yet made it to the mainstream.

What’s holding MBD back? Engineering.com contributor Mike Thomas writes about his company’s failed attempts to implement MBD in 6 reasons we still can’t switch to MBD—and the ways forward.

Got news, tips, comments, or complaints? Send them my way: malba@wtwhmedia.com.

The post AI in Onshape and on ships appeared first on Engineering.com.

What do smartphones, e-scooters, solar inverters and IoT devices have in common? They all rely on MOSFETs (Metal-Oxide-Semiconductor Field-Effect Transistors) to function. These tiny transistors are used in every device that requires a switch-mode power supply—from consumer gadgets to industrial machinery—making them one of the most essential components in modern electronics.

A MOSFET is a semiconductor device with three terminals: the source, drain, and gate. The gate, which is insulated by a thin layer of metal oxide, regulates current flow between the source and drain. When a voltage is applied to the gate, it changes the conductivity of the main circuit. Among their many uses, MOSFETs can dim lights, amplify signals, remotely control motor speeds, and automatically switch circuits on and off.

Despite their versatility, MOSFETs often face issues around balancing efficiency with thermal losses. Like most electrical components, MOSFETs generate heat during operation—and if this heat is not properly managed, it can degrade performance and shorten the lifespan of the device. The problem of heat dissipation becomes even more pressing in high-power applications.

“Today’s designs demand more power while fitting into increasingly compact devices,” says Simon Reuning, global technical marketing manager at YAGEO Group. “Engineers must navigate the complexities of efficiency, thermal management and component size — all while developing MOSFETs that can meet these growing demands.”

This article explores how YAGEO’s XSemi MOSFETs address common issues, what engineers should consider when using them, and applications where these components are most impactful.

Standout Features of YAGEO’s XSemi MOSFETs

One of the defining features of YAGEO’s XSemi MOSFETs is their ultra-low on-resistance (RDS(on)) with fast switching performance. On-resistance is the resistance between the drain and source terminals when the MOSFET is active. A lower RDS(on) minimizes conduction losses during switching, reducing the amount of energy converted into heat. This not only improves overall efficiency but also decreases the self-heating of the MOSFET, enabling it to handle higher power conditions.

“A key focus in MOSFET design is optimizing thermal dissipation,” says Reuning. “For example, we look at innovative ways to effectively channel heat away from the device.”

XSemi MOSFETs offer advanced packaging for enhancing thermal dissipation. They are also built to perform in tough conditions, such as outdoor or industrial settings where components have to withstand temperature fluctuations, moisture, and other environmental stressors. This is especially relevant in applications like e-scooters, which must maintain consistent performance when exposed to variable conditions.

Ruggedized features like enhanced avalanche energy ratings allow XSemi MOSFETs to endure high-energy events without catastrophic failure, extending the lifespan of components. These ratings improve the device’s ability to withstand energy transients caused by conditions such as voltage spikes, current surges, or load switching. This is particularly important in applications where MOSFETs operate in harsh environments, such as industrial or high-power systems.

In practical terms, avalanche capability determines how well a MOSFET can absorb excess energy without failing. When a MOSFET is exposed to a voltage that exceeds its maximum drain-source voltage, it enters the breakdown region. In most cases, this would destroy the device. However, MOSFETs with enhanced avalanche capability can handle such voltage spikes while continuing to operate within safe temperature and current limits, as defined in their datasheets.

“A high avalanche rating enhances system robustness, making power switching more reliable during transitions between different frequencies,” says Reuning.

Trade-offs Engineers Must Consider When Using MOSFETs

Engineers must navigate a delicate balance between key performance parameters when selecting the right MOSFET for their application. Whether it’s achieving lower conduction losses, faster switching speeds, or higher voltage tolerances, every decision impacts the performance of the system.

One primary consideration is the interplay between on-resistance (RDS(on)) and gate charge (Qg). Gate charge refers to the amount of charge required to activate the MOSFET by injecting charge into the gate electrode. A lower gate charge results in lower switching losses and higher switching speeds, which are particularly advantageous in high-frequency applications like motor drives or DC-DC converters. However, these designs come with higher RDS(on).

“A low gate charge enables faster switching and allows surrounding components—such as inductors and capacitors—to shrink, ultimately increasing efficiency,” explains Reuning. “However, this often comes at the cost of higher RDS(on) and reduced power-handling capabilities. Conversely, achieving low RDS(on) typically requires a larger die and a slightly higher gate charge.”

The choice of MOSFET construction further complicates the decision-making process, with each architecture bringing unique advantages and limitations. Traditional planar designs are cost-effective but may lack the advanced performance characteristics needed for high-power applications. Trench constructions optimize for low RDS(on), while double-gate designs prioritize lower gate charge and faster switching speeds. Superjunction MOSFETs offer smaller die sizes and support higher switching frequencies.

Voltage requirements also play a significant role. For instance, automotive applications increasingly demand MOSFETs capable of handling 800V systems.

“High-voltage MOSFETs inherently require higher RDS(on) and gate charge,” says Reuning. “The key challenge is determining whether the trade-off is manageable within your design constraints.”

At the end of the day, Reuning believes that the most crucial task for engineers is to carefully weigh the trade-offs and optimize their designs accordingly.

“Optimizing a MOSFET design always involves trade-offs,” says Reuning. “Low RDS(on) comes at the cost of other parameters, just as reducing gate charge requires sacrifices elsewhere. There’s no single packaging that delivers the best of everything — at least not yet. Engineers must determine which characteristics matter most for their application. For example, if high switching frequency isn’t a priority, you might tolerate a higher gate charge in exchange for improved voltage handling. Careful evaluation of these trade-offs is crucial for selecting the right components.”

Applications of XSemi MOSFETs

YAGEO’s XSemi MOSFETs have applications across many established industries and emerging markets.

“Our MOSFETs support a wide range of power applications, from EV charging stations and solar panels to battery management systems, industrial power tools, servers and telecommunications power supplies,” says Reuning. “They are also well-suited for system power, PCs, portable devices and switch-mode power supplies. With a diverse portfolio covering various case sizes — from surface mount to through-hole — and multiple voltage levels, we offer solutions tailored to different design requirements.”

As mentioned earlier, XSemi MOSFETs work well in e-scooters—not only due to their ruggedized features but also their high energy efficiency, particularly in devices like inverters and onboard chargers. The components are also integral to renewable energy systems. Solar inverters and home battery backup systems, such as battery walls, depend heavily on MOSFETs for efficient energy conversion and storage.

XSemi MOSFETs are additionally useful for IoT and edge computing applications, which involve compact, low-power solutions. The increasing miniaturization of power supplies in these fields necessitates smaller components and more energy-dense packaging.

Here too, Reuning discusses some considerations for engineers: “What trade-offs can be made for a smaller footprint? For instance, can increasing the switching frequency allow for the use of smaller components, such as inductors?”

“What trade-offs can be made for a smaller footprint? For instance, can increasing the switching frequency allow for the use of smaller components, such as inductors?”

XSemi MOSFETS have also helped manufacturers optimize power systems in real-world applications. In one case, a power supply manufacturer leveraged a 600V N-channel MOSFET with enhanced avalanche energy ratings to improve the efficiency and reliability of an inverter design. Another success story involved a motor application where a low RDS(on) MOSFET allowed for more reliable operation during high-power cycling, leading to longer operational life and improved overall performance.

YAGEO’s XSemi MOSFETs are playing a growing role in industry automation, where sensors and camera systems are being adopted to enhance productivity. As industries continue to evolve, MOSFETs will remain fundamental to meeting new power and performance demands.

To learn more, visit YAGEO at TTI.

The post How to choose the right MOSFET for the job appeared first on Engineering.com.

It takes all kinds, as the platitude says, but this sentiment applies not only to engineers, but to the next generation of engineering materials as well. A team of researchers from the University of Pennsylvania have demonstrated this first-hand by showing how adding the right amount of disorder to the structure of mechanical metamaterials can more than double their resistance to cracking.

“Toughness is a limiting factor in not all, but many 3D printed mechanical metamaterials,” said professor of mechanical engineering Kevin Turner in a Penn press release. Turner is senior author of the published research, which he says shows that, “Without changing the material at all, just simply by altering the internal geometry, you can increase the toughness by 2.6 times.”

To test whether disorder makes mechanical metamaterials tougher, Turner and his colleagues performed thousands of computational mechanics simulations of numerous variations on a standard truss. In some, the triangular lattices were arranged in perfect symmetry, while in others the patterns were perturbed by shifting their connecting nodes.

“The samples that performed the best, in which it was most difficult for a crack to grow, did not consist of regular repeating patterns,” said Sage Fulco, a postdoctoral researcher at Penn and lead author, in the same release. “They had different geometry in different areas.”

The team subjected the patterns to rounds of computer simulations and created physical versions of a representative set of geometries, including both ordered geometries and those with varying levels of disorder.

When they attempted to break the materials — in the lab and in the simulations — a clear trend emerged. “There was a specific level of disorder, so that the patterns we cut into the material looked somewhat regular but not exactly symmetrical, where we were able to achieve the highest level of performance,” Fulco said.

Essentially, the disorder prevented cracks from traveling in straight lines.

In the future, the researchers hope their findings will encourage a broader exploration of disordered patterns in mechanical metamaterials and mechanical design. “We used triangles, but this work is very fundamental,” says Fulco. “Other groups can apply it to many different geometries.”

The research is published in Proceedings of the National Academy of Sciences Nexus.

The post How disorder doubles metamaterial toughness appeared first on Engineering.com.

• About NFPA 70B
• Why NFPA 70B matters
• The role of an effective electrical maintenance program
• How effective condition-based maintenance programs can help save costs and keep your people and your installations safe
• Examples and best practices
• NFPA 70B as a pillar of your sustainability commitments

Your download is sponsored by Schneider Electric.

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While some engineering software developers have been showing off AI research and making big AI promises, Onshape has been quietly plugging away on more conventional updates—like the brand new CAM Studio.

But behind the scenes, the Onshape team is as keen on AI as everyone else. In an interview with Onshape founder and PTC chief evangelist Jon Hirschtick, Engineering.com learned that Onshape has been testing several AI features and is nearly ready to release the first: AI Advisor.

“We’re very close,” Hirschtick said. “You can see the lights on the runway.”

Here’s what we know about Onshape AI Advisor, who will have access to it and what other AI features it may herald.

What is Onshape AI Advisor?

Onshape AI Advisor is a product support chatbot. It was announced in September 2024 as a detail in a PTC press release about a strategic collaboration agreement with cloud computing provider AWS. At the time, PTC expected to release AI Advisor by the end of 2024.

“While designing, users will be able to type a question in simple, conversational language and the Onshape AI Advisor will respond with an answer or recommendation based on the resource library and provide links to additional information,” read PTC’s announcement.

Related: Applying AI in manufacturing: Q&A with Jon Hirschtick.

AI Advisor is powered by Amazon Bedrock, AWS’s service for building generative AI applications. Bedrock offers access to a variety of foundation models from AI developers including Anthropic, Meta, Mistral AI and more.

In our interview, Hirschtick confirmed that AI Advisor is built on a commercial foundation model, but declined to name which. Regardless, he emphasized that the Onshape team has tuned it for their userbase, and that every output will cite sources and provide external links.

“We’re giving much better results than you get if you ask these same questions to ChatGPT, or Perplexity, or Copilot, or Claude, or DeepSeek,” Hirschtick said.

What kind of questions can you ask Onshape AI Advisor?

Hirschtick gave some examples of how users could interact with the new AI assistant.

“How would I create a curvature continuous boundary surface in Onshape?” one user might ask.

“What features would you recommend for modelling a remote control?” another may inquire.

These are questions a user could look up in the documentation, Hirschtick admits, but “so are half the things we ask each other.” Even experienced Onshape users may not know about all the features of the oft-updated software. AI Advisor is a way to help users discover and learn new ways to design in Onshape.

It may debut as a product support chatbot, but Hirschtick says that’s just phase one for AI Advisor. In the future, users will be able to ask tailored questions and get more practical output. Hirschtick gave a few more examples.

“Can you give me ideas on how to improve the performance of this model?” asks a user, who is then shown some possible solutions.

“Write a conditional operator in Onshape that says if the trailer width is less than 28 the value should be 4, if not, the value should be 5,” prompts another, and AI Advisor gives the expression in Onshape’s variable syntax.

“The first application will just be expert advice on how to use Onshape with cited sources,” Hirschtick summarized. “Future applications may involve generating expressions, maybe someday generating API calls. It could even someday modify your model, whether it’s with text-to-CAD or other[wise].”

AI Advisor for all (for now)

We couldn’t confirm the release date for AI Advisor, but we did learn which users will have access to Onshape’s upcoming AI feature.

First, the good news: Onshape AI Advisor will launch to all Onshape subscribers, including free and educational users. That wasn’t a given—the new Onshape CAM Studio, for instance, is only available to Onshape Professional and Enterprise subscribers, plus there’s an extension called CAM Studio Advanced that will cost extra for everyone.

The chatbot’s availability may change, however. Hirschtick speculated that as AI Advisor matures and expands, some of its capabilities may be segmented by subscription tier. Time will tell, but it will probably side with Hirschtick. Given the computing cost of generative AI and the business model of SaaS, it’d be surprising if PTC kept AI Advisor free forever.

Expect more AI from Onshape—someday

The soon-to-be-released AI Advisor is just the first step Onshape will take with AI. Hirschtick said the development team is actively exploring other AI features, including AI-based rendering and generative text-to-CAD.

Onshape users shouldn’t get too excited about these tools just yet. When it comes to AI, Onshape prefers patience to flash.

“We could ship tomorrow if we wanted something that’s a demo,” Hirschtick said. “The hard part is turning these into tools that pros value in pro-level use cases. And we’re working on it.”

The post Onshape AI Advisor is coming soon—here’s everything we know appeared first on Engineering.com.

Your download is sponsored by Hawk Ridge Systems.

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The latest advancement in this area comes from engineers at Cornell University, who have discovered a way to control grain size and morphology in multiprincipal element alloys (MPEAs) during the metal AM process.

“A major problem is that most of the materials we print form column-like structures that can weaken the material in certain directions,” said Atieh Moridi in a press release. “We discovered that by adjusting the composition of the alloys, we can essentially disrupt these column-like structures and make a more uniform material.” Moridi is assistant professor in the Sibley School of Mechanical and Aerospace Engineering and senior author of the published research.

By adjusting the relative amounts of manganese and iron in their starting material, Moridi and her team team disrupted columnar grain growth, significantly reduced grain size, and improved the yield strength of the finished metal.

“Microstructural features, like grain size, are the building blocks that govern material performance and properties” Moridi said. “The material composition controls the phase stability, which was the key for us to control the microstructure.”

“The difficult part was trying to resolve these very short spans of time where the material goes from liquid state to solid state,” explained first author Akane Wakai. The team overcame this roadblock by utilizing the Cornell High Energy Synchrotron Source (CHESS) to obtain fraction-of-a-second data about their materials during the printing process. In the best-performing sample, the researchers found evidence of an intermediate phase that can help disrupt those column-like grains and refine the grain structure.

“The findings from this research can be used for real-life applications to create more reliable materials that enable even better performance,” Wakai said. “Not too far into the future, we’ll start seeing 3D printed metal parts, even in consumer products like cars or electronics.”

The research is published in the journal Nature Communications.

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