Written by Todd Tuthill, Vice President for Aerospace and Defense Strategy and Marketing, Siemens Digital Industries Software.

The enormous breakthroughs artificial intelligence (AI) has had in the past couple years are common knowledge at this point. AI itself is nothing new, but recent advances in hardware and GPU technology have enabled the operation of more sophisticated machine learning models that can handle larger amounts of data and make better predictions, pushing AI past the hype threshold into a legitimate tool to do business with.

These advancements cannot be understated as there is a massive demand for AI, opening numerous opportunities across industries.

Aerospace and defense (A&D) is not only an industry that can benefit from AI, but also one that needs it. Despite the immense growth the A&D industry is expected to profit from in the coming years, a growing worldwide workforce shortage threatens to hinder this progress, exacerbated by increased product complexity from the integration of new technologies. If A&D companies wish to stay in business in the future, they will find AI to be a necessary component in accelerating their digital transformation journeys and producing the next generation of aircraft and spacecraft.

Expecting turbulence

There is a lot to look forward to in the realm of aerospace. For instance:

• Companies are researching different forms of sustainable propulsion.
• Engineers are designing the next generation of defense aircraft.
• The United States is poised to return to the Moon and land humans on Mars.

The future of aerospace has never looked more exciting.

Yet these ambitious goals rely on new technologies, and with these new technologies comes increased product complexity. Sustainable propulsion systems for commercial aircraft and enhanced networking capabilities for drones — for example — present new design considerations that must be integrated within the same or shorter design cycles. Additionally, as product complexity increases, so does tool complexity, as new powerful software is developed to design and validate everything from the smallest microchips to wing shapes. All these considerations and tools will take engineers years to learn, and even longer to become experts in.

This comes at an inopportune time as the industry struggles to overcome a worsening workforce shortage that is spanning the globe. The Boston Consulting Group predicts that by 2030, one out of three engineering positions may go unfilled due to a lack of required skillsets. The A&D industry is expected to create all these incredible products and innovations, but it lacks the engineers to bring them to fruition.

Transforming engineering with AI

AI has the potential to counteract the worst effects of the workforce shortage and growing product complexity. At an individual level, it gives engineers new ways to do their work, transforming how they interact with tools and enabling them to gain knowledge and experience faster.

Many industries are already integrating AI copilot systems into their tools, which engineers can utilize, through conversation, to make their jobs easier. By simply asking questions with natural language, engineers can have the copilot automate workflows and clear mundane tasks, as well as quickly access the copilot’s library of specialized knowledge to better understand the software tool. This lessens the need to have an expert on hand to guide newly hired engineers.

Companies can take a step even further by integrating AI directly into design tools. Not only would the AI be able to learn from expert users of the software tool, but it could also use those experts’ workflows to streamline the use of the tool itself. This enables the AI to develop a set of best design practices to help engineers with everything from component placements to wing shape optimization. With AI helping develop better workflows, engineers will be in a better position to navigate product complexity.

All these applications culminate in multiplying the impact of engineers, allowing them to focus on higher-level engineering and critical thinking as AI handles the mundane work. Not only would this reduce the intensity of the workforce shortage, but it would also create a more attractive working environment to draw in new engineers.

Addressing trust

Despite the excitement surrounding AI, some people are still bound to be skeptical of the technology. There are those concerned about AI being unable to effectively do work that has long been done by humans and the results it generates, while others are concerned with AI’s ability to keep proprietary data secure. The latter is especially important in the A&D industry, as many programs are data sensitive or outright classified.

While these concerns are valid, they are not so different from when previous technological innovations changed how work is done. Therefore, they can be addressed. Efforts can be made to reduce the black box obscuring how an AI comes to its conclusions, as well as educate users to better understand how AI functions and where it could be best applied. Regarding proprietary data, instead of drawing on public information from the Internet, like ChatGPT, the AI of an aerospace company can be run locally. This way it only learns and accesses data from the company’s secure and proprietary data lake.

AI in digital transformation

The capabilities described earlier in this article are just the tip of the iceberg. At a higher level, AI can be the key component to help accelerate companies’ digital transformation journeys to levels once thought impossible.  

Most companies in A&D have already begun their digital transformations, making inroads in configuring model-based workflows and data archiving, as well as connecting data across engineering domains and increasing traceability. With AI, however, companies can go even further. Automating mundane tasks and streamlining engineers’ workflows with copilots and tool assistants is just the beginning. AI is already being used to generate component designs, write support documents, find optimized solutions and perform many other tasks that we once thought only humans could do. Right now, these capabilities are limited to single engineering domains, but with every leap in AI technology, they grow closer to applying to full multi-domain physics models.

As products increase in complexity and the aerospace workforce gets tighter, AI is positioned to enhance the work of human engineers and enable the A&D industry to overcome its obstacles. By the time 2030 approaches, AI will have dramatically altered the way A&D does engineering, and there will be two types of companies: those who have gone out of business and those who embraced AI.

About the Author

Todd Tuthill is the Vice President for Aerospace and Defense Strategy and Marketing at Siemens Digital Industries Software. Todd’s engineering background is in systems design with functional engineering and program leadership roles and a strong vision for digital transformation. His 30+ aerospace leadership career spans McDonnell Douglas/Boeing, Moog, Raytheon, and Siemens. His experience encompasses all aspects of A&D programs, including design, model-based systems engineering, software engineering, lean product development, supplier/partner management and program management. In his role at Siemens, he is a passionate advocate for the advancement of digital transformation across the A&D industry.

The post Why aerospace needs artificial intelligence appeared first on Engineering.com.

Written by Nand Kochhar, vice president of Automotive and Transportation for Siemens Digital Industries Software.

The U.S. autonomous car market size is estimated to reach $37.56 billion by 2029, growing at a CAGR of 20.5% between 2024 and 2029. However, the widespread implementation of automated vehicles (AVs) in the real world (Figure 1) depends on the verification and validation (V&V) for designing, manufacturing and of course, operation. Hence, AVs must safely and reliably drive on roads in all weather and traffic conditions on urban, suburban and back-country roads.

To continue the march towards autonomy, companies must transform the methods with which vehicles are developed and put into the market. While great opportunity exists in the future, companies face several impediments to supplying connected, automated and software-defined vehicles to the world.

Building AVs is a particularly complex process because the entire vehicle is a system of mechanical, electrical, electronic, network and software systems. State-of-the-art components from each of these domains are required to create the most sophisticated vehicles ever produced.

Thus, the V&V of AV safety, reliability and performance in all traffic scenarios is a daunting task. Projections indicate AV platforms will need to complete the equivalent of billions of miles of testing to ensure their safety and reliability. And, because the vehicle involves interconnected and interdependent systems, the complexity compounds.

Connecting real world and simulation data via digitalization

The development of advanced driver assistance systems and AVs is a data-driven engineering process. Numerous measurements are generated, analyzed and incorporated back into the design at each step in the lifecycle. Translating the raw data gathered into engineering insights that drive improvements and optimizations is where competitive advantages are won and lost.

Companies can successfully address the complexities of these challenges by using a mixed-reality, digitalized approach to the development, building, verification and validation of their vehicles and driving systems.

Real-world data collection is critical to providing accurate V&V. Typically, the information collected from a physical test is immense. Data-collection software can perform an initial analysis to distinguish what is pertinent to the testing objective at hand. This enables teams to make well-informed decisions on data storage and processing priority.

Essential hardware elements, sensors, actuators, controllers or complete autonomous driving systems need to be tested. Hardware components are verified and validated using simulators to mimic on-road driving scenarios with varying environments, traffic and road conditions.

Hardware-in-the-loop simulation software can help with the testing of sensor systems. These solutions can test camera-based perception systems used for advanced driver assistance systems by integrating the actual camera sensor into the testing environment. This helps increase simulation fidelity because the system is processing real sensor data. Camera projection boxes, as well as camera injection setups, allow engineers to test the camera-based perception systems under challenging conditions.

This information becomes more powerful when converted into the virtual domain. Real-world tests enrich and inform simulated vehicle environments and driving dynamics. Scenarios captured during real-world testing can be imported and recreated in a simulation solution (Figure 2). Within the simulation environment, engineers can change parameters of the scenario (such as weather conditions or vehicle speeds) so that they can interrogate all facets of system performance in different driving environments.

Using data from physical testing and simulation, AV engineers can quickly identify on-road edge cases and assess vehicle behavior in all driving scenarios. As the AV is an integrated system, its development platform also needs to be integrated to test and retest the operation of the vehicle in realistic virtual scenarios throughout the design process. By implementing both design and simulation on one platform, test results and simulation data can be readily reincorporated into the vehicle design. This produces a closed-loop feedback system that improves not only the design but also operation and physical characteristics of the vehicle.

When this approach is used, a comprehensive digital twin of the AV then can be created. This empowers an efficient closed-loop AV development lifecycle that spans from design to verification and validation and even through to in-field maintenance.

Encountering unsafe scenarios in a virtual environment, not on the road

High-fidelity simulations using a comprehensive vehicle digital twin also provide a virtual environment for identifying unknown unsafe scenarios. Engineers can combine the knowledge from known real-world situations with mathematical prediction and simulation methods to uncover possible alternative critical scenarios. They can discover and analyze these scenarios more efficiently in a virtual environment, reducing the number of unknown-unsafe scenarios and the risk incurred when deploying AVs.

As stringent regulations are adopted by governments that focus on road safety, they will likely guide the future of virtual vehicle testing and simulation technology standards to help streamline consistency and acceptance of AV certification.

Collaboration is key to building confidence

In the United States and worldwide, standards were developed for vehicle certification. As regulations matured, confidence in the products was built. The engineering environment for AVs also needs to build that confidence, to prove the connection between physical testing and virtual testing so that the authorities can confidently approve standards that will benefit everyone, manufacturers and customers alike.

Three areas are crucially important to fully autonomous vehicles being introduced successfully: public acceptance, technology and regulations. The automotive and transportation industries have a challenging task ahead of them to address the needs of all three areas.

Standards should help balance out this tension between technology and regulations. They would help guide companies, like Siemens, that develop tools for AV manufacturing companies. These tools could then help ensure that the standards are in alignment with the technology that they are developing and vice versa. Standards, and the verification and validation they call for, may be driving the speed with which we will see AVs become common.

The process of designing, building, testing and scaling autonomous cars is complex and time-consuming. As such, advancement must rely heavily on collaboration and partnerships, which will drive innovation, improve functionality and ensure safety for everyone on the road.

About the Author

Nand Kochhar is the vice president of Automotive and Transportation for Siemens Digital Industries Software. He joined Siemens in 2020 after nearly 30 years with Ford Motor Company, where he most recently served as Global Safety Systems Chief Engineer. In this capacity, Kochhar was responsible for vehicle safety performance of all Ford and Lincoln brand products globally. He also served as Executive Technical Leader, CAE and as a member of Ford’s Technology Advisory Board. Kochhar’s tenure at Ford also included executive engineering leadership across a range of disciplines including in product development, manufacturing, digitalization, simulation technology development and implementation.   

The post Digitalized verification and validation paves the way for automated vehicles appeared first on Engineering.com.

Bob Schuster is no stranger to the automotive industry. The GM veteran saw ‘the writing on the wall’ and decided it was time to make it on his own. Though already a manufacturing expert, Bob quickly trained up on Siemens technology, acquired CNC equipment — and Schuster Mechanical LLC was born. His success in the automotive field, and now adjacent industries, continues to this day.

“I’m small and attentive,” says Schuster. “Jobs that I landed were due to my reputation. I will collaborate with the assigning engineer and make sure the job is completed to their satisfaction. I’m not done until they are happy.”

But this level of success, especially for an individual, does not come unexpectedly. Schuster says that much of what he does is made possible using Siemens Zel X, because it enables him to foster and grow close relationships with his clients.

Schuster Mechanical’s secret to success is helping the “big guys”

Schuster Mechanical takes on the important, but small, jobs that big CNC outlets do not want. “They want volume,” Schuster says. “They can’t do smaller stuff like I can.” However, large manufacturers often need one-off replacements for old machinery. This important but underserved niche is where Schuster Mechanical flourishes.

As a one-man-show, its Schuster’s skills, flexibility and adaptability that separates his shop from the rest. If customers call him to a production facility to help design a tool, replace a broken part or make minor changes to a line, he shows up when needed. “Afterall, you can’t stop production,” he says. “I will see the existing design and propose a new one.”

Schuster’s close, attentive and accurate services do not come cheap. But his skill, work and adaptability speak for themselves. “Right now, there is a shortage of available shops,” says Schuster. “But you can’t do any job at the door. You need to be well suited … [its about] having a good relationship and getting the work you like. I’m not doing precision, ultra precision [or volume] work. I’m doing the other work. You have to match the work at the door to your capabilities.”

When makers match work to their capabilities it goes beyond their personal skills; it also boils down to the tools they use. To overcome most of those capability challenges, Schuster has turned to Zel X.

What Schuster Mechanical makes and the challenges it faces

Schuster mostly makes new grippers or effectors and installs them to legacy equipment. These parts must be custom designed to grab, mount and move parts during manufacturing and assembly.

Often customers only need one part; however, this does not minimize its importance as it may be used to make hundreds of thousands of products a day. These new parts could replace ones at the end of their lifecycle or adapt equipment to make new products — or even multiple products.

“My job is to build the fixture and it’s important for me to design it properly and deliver it on time,” he says. This accuracy and time sensitivity is key. If the part is not ready on time, then it may not be available for installation during a plant’s scheduled maintenance. As a result, the customer could lose millions of dollars waiting for the part. Additionally, if the part does not work as designed, equipment may fail, or products may come out of spec. This would also cost customers millions as they try to fix the issue on the fly.

Another challenge Schuster faced when making new parts for old equipment is that the reference data you need is often inexact, incomplete or non-existent. Sometimes the equipment he adapts could have reference manuals hundreds of pages long and he only needs to extract a few details to make the part. However, if the customer made prior changes to the equipment, that reference manual may be out of date.

Sometimes, the only way to get the information Schuster needs is to see and access the current installation in person. However, accessing the part can be hard and dangerous. During early development, Schuster explains, he must “find a window, maybe 20 minutes, to get in there and take it apart, take pictures, put it back together and take all that data to build my improved part.”

He also often crawls into the equipment a second time to install his new part. During installation, he has “been in [situations where] I thought I had time to finish a project and I was fabricating on the fly … suddenly I was told this line has to run. I had minutes to get it going and fortunately I landed on my feet, and it worked.”

How Schuster Mechanical confronts development cycle hiccups

It is of course best to avoid challenges like the ones noted above. Though issues will arise with any project, Schuster and his customers can foresee and address most of them early in development using better means of communication.

“I address these challenges by being honest with the customer,” says Schuster. “When I look at the data and there is a misunderstanding … I call the engineer right away and verify.” He notes that after one of these moments of honesty, the customer involved ordered eight new parts the very next day. “Pointing out the error made them happy; they trusted me.”

Schuster also uses Zel X to bridge communication gaps with customers and peers. “Zel X makes collaboration instantaneous. [With email you] needed to export CAD, describe it in PDF and the person on the other end needs to interpret that. That’s a time-consuming process. With Zel X, I still need to do some of the same steps … but instead of an email, the data updates and shares. We can communicate on the phone while looking at the same data and make decisions faster.”

In fact, this improved form of communication has enabled Schuster to reduce on-site visits. “I get to design much sooner,” he says. “I have the ability to do a quick design review and get started. These were done in person before. Now this happens much less.”

Zel X also has tools that stakeholders can use to markup, comment and revise data. As a result, Schuster is up to date on what the customer is up to, and his customers knows where he is with a project. Thus, the design process runs smoothly without in-person interactions. “The markup tools are built-in and we are able to talk back and forth and steps are recorded. Misunderstandings were honest before, but now you can see it written so we understand and finish much faster.”

When things do go wrong, Zel X is also helpful. It enables Schuster’s customers to keep him aware about how his parts are running. “The sooner I know it’s successful the better,” he says. “I need feedback on any concerns right away. Zel X is the way to do that. [With it, customers can] communicate with me like in the review process. If something was overlooked — sometimes by one or both of us — it’s usually very simple to punch out these little details. It’s easier to correct these faster [in Zel X] than with email.”

What else does Schuster Mechanical do with Zel X?

Another big challenge machine shops face is ensuring that a customer’s data is secure, tracible, accessible and available in a way that fosters collaboration between stakeholders. It has built-in tools for collaboration. When I put data into it, its more secure than it was before.”

Schuster also uses features in the software for quoting, restricting data access (based on a user’s credentials) and storing information safely on the cloud. If he or any stakeholder needs to look up data on the system, they must use two-step authentication. This ensures users only see the data they have permissions to see.

“It’s a step in the right direction and long overdue, like 20 years overdue,” he says. “The methods we shared things with in the past weren’t good.  Almost all shops can do the work effectively, but customers need to be comfortable their data is in good hands. They trust Siemens to be a good steward of their data, and I think they are. That’s a good thing for everybody.”

As for Zel X’s quoting abilities, Schuster says, “The quoting process marches along much more efficiently and quickly. It can shorten the quoting process and make it more comfortable and safer for both parties … Everything is recorded.

To learn more about how Zel X, click here

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One of the biggest challenges faced by legacy industrial companies is the transition into a digital age. Effectively digitizing your products and business can be a big hurdle, especially for small- and medium-sized businesses.

But Italian industrial furnace company Gadda Group has done just that, taking their more than 45-year-old family business into a more digitized world. Key to that transition has been the company’s use of Siemens’ Solid Edge and Teamcenter software mixed with an Industry 4.0 approach.

From garage to factory

Founded in 1978 by Rocco Vincenzo Adorisio, Gadda Group—which is still a family-owned business—has been producing industrial furnaces for more than 45 years. It had a humble beginning.

“My father started in a garage to produce furnaces,” Rocco’s son Stefano Adorisio told Engineering.com. “In 2010, me and my two brothers joined the company. Since then, we have grown a lot and guided the company into the digital era.”

Stefano is now an owner, proposal engineer and project manager at Gadda. He works alongside his brother Andrea Adorisio, a production manager, and his brother Simone Adorisio, general manager. They are continuing their family legacy of designing, manufacturing and installing industrial furnaces, while also making sure their products remain cutting edge. Gadda also manufactures many pieces of equipment needed to support a furnace operation, including automatic loading machines and quenching tanks.

Today Gadda has 50 employees, including engineers, production workmen and technical support, with more than 6,000 square meters of production area in the Italian provinces of Turin and Milan. The company has completed more than 600 projects for more than 400 companies in over 20 countries.

Making custom furnaces with Solid Edge

Gadda does not take on a large number of projects, typically only producing around 30 units a year.

“We do not have a catalog. We have only references. So, every piece of equipment is tailor made for every single customer,” Stefano Adorisio says.

Gadda’s furnaces can heat parts up to 1,300° C inside of variously sized chambers, each designed specifically for a customer’s needs. The company’s clients represent a variety of industries including aerospace, automotive, and oil and gas, all needing to heat treat materials ranging from steel to titanium alloy.

Two of Gadda’s custom furnaces have already debuted this year for Italian customers. The Bogie Hearth Furnaces will be used for heating large ingots up to 1,300° C and for forging. The larger of the two furnaces includes a 450-ton capacity and 11 pairs of flat flame regenerative burners. Gadda focused on ease of maintenance and safety in these designs.

Customization for each client requires a high level of design work, which for Gadda takes place in Siemens’ Solid Edge CAD software. Adorisio says that typically half of each project is constructed from standard parts and designs from other projects, while the other half is tailor made for each customer. It typically takes Gadda around one year to complete a furnace from when the order is placed to when the furnace is commissioned.

Adopting Solid Edge as the company’s design software has enabled the business to scale while taking this custom design approach. Gadda first explored using Solid Edge back in 2000 when a young engineer recommended that the organization purchase a single license of the software.

Gadda never looked back. Today the company has ten licenses of Solid Edge thanks to its partnership with Italian Siemens reseller Novasystem. All of Gadda’s complex, custom industrial furnace models now come to life in Solid Edge. The engineering team relies on simulation tools within Solid Edge, as well as the XpresRoute tool to simplify the piping and routing design process.

Gadda also adopted Siemens’ product lifecycle management (PLM) software, Teamcenter, after encountering issues with their previous system of design management.

“When two to three people were working on the same project, it was difficult to define who would do some parts and who would do other parts,” Adorisio recalls.

The company switched to Teamcenter in 2019 and saw immediate improvements. This software helped elevate the Gadda team’s collaboration and was an important asset for Gadda during the COVID-19 pandemic, when many of their employees worked remotely.

“With Teamcenter, it is very easy because of their system to check in and check out parts,” Adorisio says. “Everyone knows who is making some modifications to the project, and it also is easy to work at home.”

Firing up Industry 4.0

Being part of the business for decades, Gadda has continued to innovate, pushing their designs to ensure their products fit in with the evolving Industry 4.0 world and the modern smart factory.

“Industry 4.0 has become a standard for us because every customer today needs good automatization and a robust system to communicate with the equipment,” Adorisio says.

Gadda has incorporated many sensors into their industrial furnaces that can assist with predictive maintenance and improve safety, especially in their automated systems. Automation is even helping to reduce human error and safety risks for operators, who now play a more supervisory role, with things like heat-treatment recipes able to be programmed into the system remotely.

“I think that new sensors, new PLCs and new communication can help our equipment to reach higher performance in terms of efficiency and in terms of productivity. This was a step made together with all stakeholders: our customer, our supplier and ourselves,” Adorisio says.

The Italian government has also been a driving factor in pushing companies towards the Industry 4.0 approach and beyond. Italy launched Industria 4.0 in 2016, a national strategy for digitizing industry and encouraging new investment from domestic and international organizations.

Now, Italy and Gadda are both looking at the next step beyond Industry 4.0 and what it means for the country and company.

“In Italy, we are speaking about Industry 5.0, which includes artificial intelligence,” Adorisio explains. “I think that in the next five years, some of that new technology will also be part for our equipment.”

Thanks to modern design software and tools, this family-owned business plans to evolve and stay at the cutting edge of their industry.

Want to learn more about how you can adopt Solid Edge and Teamcenter in your business? Visit the Siemens website here.

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Traditional scanning electron microscopes (SEMs) are large, expensive and complex pieces of lab equipment. To use, maintain and repair them would require specialized knowledge and skills few companies and research outlets have. This didn’t sit well with Semplor, makers of NANOS — an easy-to-use SEM the size of a desktop computer.

Making SEM available to “anybody, anywhere in the world, that’s our goal,” says Emile Asselbergs, CEO and co-owner of Semplor. “Like a modern [cellphone,] you don’t need a training course to use it. It’s easy, fast and doesn’t break.”

The Netherlands-based company achieved this paradigm shift by rethinking SEM technology and designing the NANOS with completely new parts, methodologies and workflows. In other words, they redesigned SEM from the ground up. However, as a small startup, Semplor didn’t have the budget for most engineering design software.

“With Enginia we were in contact about the Solid Edge Startup bundle,” said Asselbergs. “In this period, we were in touch with Enginia about the possibilities of the Solid Edge license, and their customer service helped with the installation and use of [the] Solid Edge Standard Parts Library.”

With this software Semplor made something as complex as an SEM into a machine that looks right at home in any startup, SME, makerspace, big-name research lab and everywhere in between.

SEM experts think the equipment is too complex

Asselbergs explains that traditional SEM equipment can be hard to maintain. “You open them up and they are large, complex, with lots of circuit boards. It needs a lot of maintenance.”

While working in the SEM field, Asselbergs and a few colleagues realized that the equipment doesn’t have to be this complicated. “We’ve been thinking about how to ideally design a table-top SEM. We didn’t use the things of the past or things we didn’t like; instead, we made a new concept,” he says “Organizations have difficulty changing mindsets. That is what we love to do.”

Asselbergs’ colleagues soon became co-founders with a shared goal: to use their combined 200 years of experience with SEM to democratize the field. They understood the common user pain points and wanted to build something better. “It’s what people need these days; when you have a problem, you want to solve it now.”

Aside from the physical design of the NANOS, its software user interface also plays a big role in simplifying SEM. The UI has a lot of automatic functions to optimize brightness, contrast and illumination. It also guides the user through the process so that those new to SEM can catch on quickly. However, it also contains many of the power-user features used by seasoned veterans in the field.

An example of how the NANOS democratizes SEM can be seen with how it deals with living samples. These samples contain a lot of water and are sensitive to the beam of electrons. As a result, they will shrivel in the high vacuum and sustain damage from the beam. Traditional equipment overcomes this challenge by coating living samples in gold, platinum and other expensive materials. Aside from the added expense, this process also complicates the assessment of these sample.

Newer SEMs have an option to operate at low vacuums, which ensures that living samples don’t shrivel and the charge they experience is neutralized. But Asselbergs says this feature is an expensive add-on for traditional SEM offerings. For the NANOS, it comes standard without added expense. This is in part thanks to how the equipment was designed.

“We don’t use vacuum gages, just good calculations,” says Asselbergs. “By the pump power and RPM, we know the vacuum level. Measuring gages cost money and can break, so that’s something we don’t want to use.”

Simplifying SEM for the masses while maintaining functionality

Scrapping the vacuum gage demonstrates the strategy Semplor used when designing the NANOS: If it’s not there, it can’t break or take up space. This enabled the design team to reduce the size of the SEM while also improving on its reliability.

This philosophy, Asselbergs explains sets the NANOS apart from other tabletop SEM devices. “They are basically low-end systems and look like a large microscope shrunk into a smaller one. They are very traditional when you open them up.”

He admits that some aspects of the NANOS are similar to other SEMs. “Some things are physics, you can’t change that. But [the NANOS has] all new systems with today’s technology. All software and electronics are from the last three years.”

Asselbergs team produced many calculations and design permutations, early in development, to optimize the NANOS. For instance, a material might be well accepted by other SEM manufacturers for a given application, but Semplor asked: would another lighter, smaller or less expensive one work just as well?

This process of testing the assumptions of the SEM industry was then applied to all other aspects of the NANOS. Could it get comparable magnifications with fewer lenses? Can an off-the-shelf camera be used to navigate a sample? Could the number of cable connections be reduced by using one PCB to control the whole system?

“The mechanics are completely redesigned,” says Asselbergs. “The electro column is assembled more like the engine of a car [than traditional SEM]. It’s not stapled cylinder segments. It’s much simpler and works beautifully.”

Simplifying the traditional SEM design doesn’t mean the NANOS is short on advanced features and capabilities. In fact, it is the opposite, and the redesign enabled Semplor to develop new features and future proof the final product.

For instance, the NANOS contains an eucentric stage that keeps the sample in focus as its tilted. Thanks to this stage every sensor currently aimed at the sample will remain aligned and focused; all that changes as the sample tilts is the spot they are pointed to. In more traditional equipment, tilting the sample would require the user to change SEM settings to refocus the sample.

As for future proofing the NANOS design, Asselbergs says, “We designed a system that has a lot of space in the column to add more sensors that can aim at the same focal point as the other sensors and equipment. So, we can make it a more high-end system. You need to design for the future from the start.”

The role of engineering software in designing the NANOS

The main design team on the NANOS consisted of one physicist and one mechanical engineer. However, these individuals were far from alone when developing the equipment. They received help from Siemens, Enginia and even Semplor’s manufacturing partners.

“The system was designed and engineered with the people that would manufacture the system,” says Asselbergs. “We knew what they had in terms of manufacturing machines and based on that knowledge we designed the system for them to manufacture and assemble.”

Solid Edge was used to track the design, manufacturing and building logistics. This ensured all stakeholders, internal and external, were aligned. “We do nothing on paper,” says Asselbergs. “You talk to many suppliers and can have a meeting five minutes long without being in the same physical spot. Many suppliers have CAM systems to operate machine millings and lathes and it’s been my goal to work with those suppliers without making 2D drawings with tolerances.”

The 3D models made in Solid Edge also helped Semplor ensure that the NANOS was easy to maintain. For instance, designers were able to ensure that parts could be easily accessed and changed out. “It helps if you have a good 3D model of your system to assess if parts are easily accessible,” says Asselbergs. “We knew the system before we built it. If a PCB breaks, users untighten four bolts and a few connectors, put in the new PCB and send the old one in for a refund. You can replay this maintenance in Solid Edge to make sure it works, is easy and is optimized.”

Solid Edge didn’t just simplify the maintenance, logistics, prototyping and manufacturing of the NANOS, it also reduced the number of physical prototypes needed via simulations. Part stiffness, modal vibrations and other mechanical assessments were done using Solid Edge. Meanwhile, many of the field calculations and electron beam simulations were made using a separate academic software.

 “A lot of simulations were done before parts were made,” says Asselbergs. “We looked at 10 different setups to optimize the design.” Regarding Solid Edge, Siemens and Enginia specifically, Asselbergs adds, “they helped us design the system to work on the first go. We’re very happy with it.”

This partnership is bound to continue. Since Semplor’s SEM design is designed to handle more sensors, it will need to optimize those setups as they are developed. What future capabilities the NANOS will have is a mystery — for now. But no doubt the work to design it will continue within Solid Edge and with the help of Siemens and Engina.

To learn how Solid Edge can help the development of your product, visit Siemens.

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Brazilian company ZEMA has been a staple in the grinding machine industry since 1953, shortly after the first machines surfaced. This certainly says something about the company as they have a long-standing presence, specializing in the design and production of CNC grinding machines.

ZEMA FLEXA corundum grinding machine. (Image: ZEMA.)

ZEMA, owned by the JUNKER Group, manufactures CNC grinding machines for the automotive industry as well as for diverse industrial sectors, such as grinding components for the aerospace industry, agriculture and steel industries, among others. Their machines consist of more than 5,000 components, so designing and manufacturing them is no small task. Currently, Zema has more than 35,000 Solid Edge drawings being managed on the Teamcenter platform. ZEMA uses Siemens’ Solid Edge for 3D design and Teamcenter for collaboration and data management, in addition to other solutions.

ZEMA’s engineering team designs all the components for the CNC grinding machines, including the casted parts such as the machine bed and column and their casting mold, the manufactured steel parts and sheet metal parts, while outsourcing some commercial components to suppliers.

CNC and Solid Edge for manufacturing planes, trains and automobiles

The first NC machines date back to the 1940s. John T. Parsons is credited with inventing the first NC machine in 1949 with help from aircraft engineer Frank L. Stulen, which was used to machine helicopter blades using mathematically developed aero foil shapes. A group of researchers from Massachusetts Institute of Technology then developed the first CNC machine in 1952.

John Parsons with Frank L. Stulen and the first NC machine. (Image: The Wood Temple.)

Today CNC continues to be a popular manufacturing process to help manufacture components for planes, trains and automobiles.

“ZEMA transitioned from using 2D drawings generated from AutoCAD to Solid Edge in 2010,” says Edward Coltri, engineering general manager at ZEMA.

Coltri was in a different position with ZEMA at the time, and his colleague says it took the design team about three months to get up and running with Solid Edge with the help from their local reseller, PLMX. They were surprised how quickly it went.

Coltri says PLMX was the primary reason that ZEMA chose Solid Edge, in addition to the capabilities of the software, and was very supportive through the transition period. He says the feedback from the design team is that the learning curve for Solid Edge was quick, and that “Solid Edge is easy and quick to work with, and with the updates, it keeps getting better with each version.”

Over the years, ZEMA has released various models of their machines, and their customers regularly come back to negotiate with ZEMA to retrofit their machines. Because it is an older machine, the original drawings were created in a different format than Solid Edge. The migration to the new Teamcenter solution made it easier to manage this legacy data.

The company has been using Teamcenter since 2022, and last year were able to incorporate all the data from one of their latest machine designs into Teamcenter. Coltri says originally it took about eight months to use Teamcenter to its full potential.

Complete 3D model in Solid Edge of a ZEMA machine, including all subgroups. (Image: ZEMA.)

“With Solid Edge, we are able to easily convert a drawing and provide a 3D model, which is one advantage of the software,” Coltri says.

It’s been their go-to tool, not only for collaborating internally, but also for when external suppliers send neutral formats as these can be read by Solid Edge and stored in Teamcenter for easy review.

“Solid Edge data can be easily exchanged with other software from our suppliers,” says Coltri. “Teamcenter is on the desktop and the data is coming from a local main server. Other features we appreciate are the interface between CAD and CAM, and checking for interference between machine groups. We also use it for virtual reality, video creation and simulation.”

Teamcenter is also integrated with their shop floor to aid in producing the parts and in the assembly of the machines. Clashes between parts are caught by Solid Edge.

Cast iron assembly groups for the machine spindle grinding unit, managed in Teamcenter. (Image: ZEMA.)

With Teamcenter, the ZEMA team can easily create a bill of materials and put that into their enterprise resource planning (ERP) system.

Coltri says they are currently working with PLMX to integrate set drawings and lists of materials with external systems, and with the automatic loading of information into the company’s ERP system.

Overall, he says, “we are very happy with the software from Siemens. And we have very good support from PLMX.”

ZEMA at the Sao Paulo headquarters with Edward Coltri and design team members Dirk Huber, Erica Taira, Thais Hachul, Richard Runnells, Dan Staples and Georgia Berardi. (Image: ZEMA.)

Griding machines for many applications

If you’re interested in grinding machines, ZEMA offers a full range of options. The company is based in São Paulo, Brazil, and currently has around 105 employees. They typically produce about 25 machines per year. They use the JUNKER Group’s international distribution and service network, and operate mainly for automotive and tool customers.

Their corundum grinding machines fulfill the requirements of series production for a wide range of different workpieces. The CNC grinding machines grind elements such as flanges and journals on crankshafts, as well as gear, turbocharger and cardan shafts with the utmost precision and reliability.

ZEMA produces NUMERIKA and KARGO brands. See here for more information on ZEMA’s grinding machines.

Learn more about Solid Edge from Siemens.

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Science-fiction portrays many exciting ways engineering can change in the far future. In the Marvel Cinematic Universe, Tony Stark designs and modifies his signature suits by conversing with his digital assistant J.A.R.V.I.S. In Star Trek: Voyager, the crew uses the franchise-famous holodeck to create a virtual representation of a custom shuttle design that changes to match the crew’s spoken decisions in real-time. A keyboard and mouse are nowhere to be seen in these examples.

(Image: iStock/Dima Zel.)

The aerospace and defense (A&D) industry is still far away from using something as sophisticated as J.A.R.V.I.S. or the holodeck to design aircraft and spacecraft, but that is starting to change with the rise of artificial intelligence. AI is already transforming existing engineering processes and can be the key to unlocking new capabilities that were once limited to the realm of science-fiction. Investing in the technology now can not only enable the A&D industry to overcome more immediate problems, but it can also bring the future closer to the present.

AI in today’s tools

Already, AI is being integrated into software tools to accelerate engineering workflows. As copilots and digital assistants, AI can carry out commands given by engineers using natural language models (LLMs). Instead of excessively clicking, engineers speak to their tools and the copilot automatically completes mundane tasks, such as data management, giving engineers more time to focus on higher-level tasks.

Additionally, AI can be helpful in teaching the next generation of aerospace engineers. The copilot can quickly access specialized libraries and information, enabling new users to learn tools faster. More incredible is how AI can learn from expert users. It can assess their workflows to streamline tasks or create a set of best practices. This can help future engineers with everything from component placements to geometry optimization.

Integrating AI into software tools can enable users to become experts faster. (Image: iStock/Gorodendkoff.)

These capabilities can be particularly beneficial to the A&D industry, which is facing a growing workforce crisis. According to a PWC/AIA study, “Over 29% of the industry’s workforce is over the age of 55, creating waves of retirement impact that will last 10-20 years into the future.” This presents a problem, as experts will need to onboard new aerospace engineers. Having AI-integrated tools based on the knowledge of these veterans can reduce the loss of expertise when they retire by enabling newcomers to quickly master expert software, tools, information and habits.

AI on the horizon

If speaking to your computer to automatically generate designs sounds like interacting with J.A.R.V.I.S., you would be correct. While nowhere near as sophisticated as what is shown in movies or television, the capabilities AI is bringing today are the stepping-stones toward this potential future.

With continued investment, companies can find ways to use AI as the key to enhance their digital transformations. Though generative design tools have been around for a long time, the tasks they automate are largely confined to single engineering domains and product development steps. As AI technology and its data management capacities improve, however, perhaps it will organize data across tools and domains to create a universal model for physics-based generative design. Furthermore, these data models may also self-validate hundreds, or perhaps thousands of times, until a design is as optimal as it can be.

Futuristic engineering capabilities may be limited to science-fiction, but AI is the stepping-stone toward realizing them. (Image: Siemens.)

Also, when combined with augmented or virtual reality, a new level of immersive engineering can be achieved. Engineers can communicate in a virtual environment from anywhere in the world to coordinate on the design of their product. With AI in the picture, the participants could see a virtual representation of their designs that updates in real-time based on spoken commands. Does that not sound like the holodeck?

The A&D industry has so many exciting innovations being developed, from sustainable propulsion and next-generation defense aircraft to space programs bringing humanity, where no one has gone before. AI will not invent the technologies to achieve these things, but it will enable people to bring those technologies to fruition sooner rather than later.

Getting people onboard

Of course, adopting AI to the levels described above will not be without its obstacles. Within A&D, as well as many other industries, efforts must be made to secure both trust and fluency in the technology.

Like any new revolutionary technology, there are people who are skeptical about AI. The A&D industry has carried out engineering processes in particular ways for decades. While engineers are curious thinkers, some are still bound to trust traditional methodologies over AI’s capabilities, especially if they do not understand them.

AI offers numerous opportunities to accelerate aerospace engineering processes and bring the industry to new heights. (Image: Siemens.)

Other concerns about trust arise from data security. The A&D industry in particular deals in proprietary data. If AI is going to be utilized to design a new component, for example, then companies will want to be sure their data is not exposed to third parties.

As technology continues to prove its own success, however, people will eventually see the good sides of AI and learn to trust it. The steps to achieving that are relatively simple. To start, the question of proprietary data is more about infrastructure than AI. Tools utilizing AI can easily be made to draw strictly from a company’s private data lake, keeping data and intellectual property safely within the company.

To accelerate people’s understanding, meanwhile, education efforts can be expanded to teach engineers how to use AI more effectively — such as how to enter the right prompts. Fortunately, there are also multiple educational courses for AI that are freely available on the Internet, so the opportunities already exist and are easy to access.

It is especially crucial that aerospace engineers become more conversant with AI, as well as software in general. Gone are the days of the accounting team calling the IT department to deal with computer problems. The engineering world is in a similar place. Software is already being integrated with contemporary, cross-domain engineering processes and products and that will only become more prevalent with the introduction of AI.

Clear skies ahead

The good news is interest in AI is alive and well. Companies appear ready to overcome these concerns. The Wall Street Journal reports that, “In the next 12 months, 43% of U.S. companies with at least $1 billion in annual revenue expect to invest at least $100 million in generative AI, according to a survey of 220 companies published … by KPMG.” The potential for radical transformation accompanying AI is real and acknowledged by the world’s biggest companies. The A&D industry can reap the rewards, too.

Whether through chatbots, tool assistants, new forms of generative design or something not yet discovered, AI offers numerous opportunities to accelerate aerospace engineering processes and bring the industry to new heights.

J.A.R.V.I.S. and the holodeck may still be confined to the realm of science-fiction, but every new leap in AI technology brings them closer and closer to reality.

Learn more about AI in industry at Siemens and the Siemens podcast “A Forward Look on AI in A&D” Part 1 and Part 2.

About the Authors

Todd Tuthill, Vice President of Aerospace and Defense at Siemens Digital Industries Software.

Barclay Brown, Ph.D., ESEP, Associate Director for AI Research for Collins Aerospace and leader for the AI Systems Working Group at INCOSE.

The post Bringing Aerospace Engineering into the Future with AI appeared first on Engineering.com.

In today’s rapidly evolving technological landscape, the intersection between education and industry is more critical than ever. Whether you’re talking about the skills gap in industry or how to recruit, stimulate and retain younger employees, it has become apparent that larger industry needs to work with educators and the educational system in order to be successful.

The “changing of the guard” from generation to generation has been somewhat short-circuited as of late. While there is no specific place to lay the blame for this lack of generational hand-off in industry — some blame the older generations, some the younger, some blame Covid and some just blame technology — it’s undeniable that industrial leaders need help onboarding Gen Z to the world of engineering and manufacturing.

Jason Bruns helps educators and industry connect through his work at the Minnesota State Engineering Center of Excellence.

Jason Bruns, director at the Minnesota State Engineering Center of Excellence, sheds light on the imperative need for collaboration between these two domains in order to adequately prepare the future workforce. “We need industry to engage with educators and with students to provide the environment to have that collaboration, to have that connection, to have that knowledge,” he says. As Gen Z enters the workforce and the paradigm shifts in technological advancement, the need for these collaborations is becoming more and more apparent.

Understanding the dynamics of Gen Z in the workforce

Bruns emphasizes the unique characteristics of Gen Z as a cohort defined by its digital nativity and dynamic worldview. Unlike previous generations, Gen Z craves purpose-driven engagement and social responsibility from the organizations they associate with.

Through his work at the Minnesota State Engineering Center of Excellence, Bruns works with both educators and industry professionals to help both parties connect. He works to create opportunities to drive student interest in industry while also showcasing their capabilities and talents to industry experts.

While well-known robot competitions are a great way for industry to connect with students, Bruns finds that those opportunities can be limiting — they can be expensive and cater to a specific type of STEM student and skillset. Bruns and his team aim to connect students with more than just robotics, and create a broad-based application of concepts and skills. The skills centered on robotics, while useful, can relegate students to a limited scope of engineering — sometimes making things overly complex to complete limited tasks.

“At the Engineering Center of Excellence, we create things like an engineering machine design competition — basically challenging students to make a chain reaction machine,” he says. Similar to a Rube Goldberg Machine, the competition that the Center of Excellence has created leverages a variety of engineering and manufacturing disciplines – providing students with a holistic method of learning and connecting to varying industrial concepts and encouraging them to consider systems rather than singular tasks.

“They can download the design challenge for free. All the rubrics are provided,” he says.

These competitions give industry leaders a valuable opportunity to mentor and connect with students in an almost passive networking process. As students connect with industry partners, they learn more about what the organizations do, as well as understand what kind of people work within these organizations. While the Center of Excellence competitions aren’t designed to directly recruit students, they do provide a valuable runway to connect students with industry organically.

These competitions are just a small piece of the connection Bruns helps facilitate between education and industry. Bruns and his team work with a number of educators and organizations to help connect the dots between the two spaces.

Bruns explains how he understands the challenge for business owners to participate in programs like this. Afterall, most of our industry has an intense obsession with developing more and more efficiency — it doesn’t feel very efficient to pull an employee off a machine or away from their design to go volunteer at a local high school.  But industry participation in this type of mentorship is vital.  “It’ll come back to bite you later, if you don’t,” he says.

Bruns works with industry professionals to help understand the best educational methods and better ways to recruit Gen Z. (Image: Jason Bruns.)

Bruns also underscores how important it is for industries to adapt their recruitment and retention strategies to align with the values and preferences of the younger generation. “The onboarding process with regards to companies is so critical and that sometimes falls by the wayside,” he says. “What’s the most critical time for a new employee? Probably the first couple weeks. Do they feel welcomed?”

He advocates for creating growth opportunities within organizations that resonate with the digital-centric mindset of Gen Z and highlight the pivotal role of technology, particularly artificial intelligence (AI), in shaping future job roles.

“The businesses that engage and provide these opportunities are going to be successful. Think of it this way: Fear is based on one of two things. Things I can’t control, or things I don’t understand,” he adds.

“Eliminate one of those two and there is no longer any fear. So, envision a young person that is now familiar with your organization. If they’re familiar with you, they’ll seek you out because they’re familiar with you. You’ve eliminated the fear. Again, the individuals that connect with the students through outreach opportunities — capstone projects, tours, internships, all those things — reap the rewards because they are engaging. They’re helping remove that fear, that misunderstanding or lack of knowledge.”

The evolution of industry roles and educational preparation

As technological advancements redefine industry landscapes, traditional job roles are undergoing a transformation. For decades, there used to be an unspoken war between the engineers in the front office and the machinists out in the shop. These days, there is more crossover and collaboration between varying disciplines in a business. Engineers are becoming marketers, operators are becoming designers, etc.

Bruns explains how engineers, for instance, are no longer confined to desk-bound tasks but are increasingly involved in hands-on collaborative efforts, facilitated by innovations like 3D printing. Design for manufacturability is more than just a buzzword, it has become a vital piece to the success of many businesses and these cross-discipline efforts help.

While many Gen Zs may not know exactly how their iPhone is manufactured, many of them have experienced 3D printing. Bruns stresses the significance of integrating emerging technologies into educational curricula to equip students with the requisite skills for future employment while also encouraging employers to incorporate some of those technologies into the engineering workspace. Adding technology that students — aka future employees — are already familiar and comfortable with creates an even smoother runway for connecting and getting students excited about industry.

Students will connect with newer technologies, see them in a practical setting (rather than just 3D printing tchotchkes) and recognize the value in both the emerging tech and the older system of processes/machine that are necessary for business to run. The gap between the classroom and the industrial world becomes easier to bridge in this way.

Bruns discusses the value of an industry-education collaboration in hopes of making recruiting and retaining Gen Z employees simpler. (Image: Jason Bruns.)

Industry-Education Collaboration Is a Path to Success

While industry-education collaboration isn’t a new concept, it often gets pushed to the wayside. That’s why Bruns strongly advocates for enhanced collaboration between industry and education through initiatives such as apprenticeships, internships and experiential learning opportunities. By opening doors for students to gain firsthand exposure to industry practices, organizations can play a pivotal role in nurturing talent pipelines. He emphasizes the importance of proactive engagement from both sides, with industry providing resources and support to educational institutions and educators leveraging industry expertise to enrich their teaching.

Drawing from his experiences as an engineer, Bruns highlights the partnership between Siemens and educational institutions through the Siemens’ Solid Edge educational resources. The initiative offers access to industry-standard CAD software, project-based curriculum, and industry certifications that empower students to develop essential skills aligned with Industry 4.0 principles. Additionally, it provides the CAD resources needed for competition and educational efforts that the Center for Excellence is offering to students. These collaborations are what bridge the gap between theoretical knowledge and practical application, thereby enhancing students’ employability and industry readiness, not to mention the quality of their projects.

“I connected with Siemens through the curriculum created for secondary school students,” says Bruns. “It’s project-problem-based… it’s the only thing that I have found that teaches Industry 4.0. The CAD is free. The curriculum is free. And there aren’t many things out there that provide this kind of opportunity.”

In the context of Industry 4.0, Bruns emphasizes the importance of equipping students with automation, robotics and cloud-based learning skills. He underscores the need for educators to embrace innovative teaching methods that align with the rapidly evolving technological landscape. By integrating industry-relevant content into educational curricula, institutions can prepare students to navigate the complexities of modern engineering and manufacturing environments effectively.

Key strategies for engaging industry in education

Bruns offers practical advice for educators seeking to engage industry partners effectively, such as leveraging local chambers of commerce as conduits for connecting with industry stakeholders and identifying opportunities for collaboration. By aligning educational initiatives with industry needs and fostering meaningful partnerships, educators can cultivate a culture of continuous learning and innovation.

“Industry defines if we, as teachers, are successful in creating a good workforce. Well, with Siemens, we have industry investing in creating and providing curriculum and software that young people in schools can utilize within their school,” he says.

Utilizing resources that larger corporations have developed for education is part of the puzzle, but the other piece goes back to that familiarization of the businesses and industries. Involving ground-level employees in mentorship or collaboration with educators will pay off in the long-term, according to Bruns.

By fostering partnerships between industry and education, he advocates for a holistic approach to prepare students for the evolving demands of the workforce. Collaboration between education and industry in preparing the future workforce is of critical importance. By embracing innovative teaching methodologies, fostering experiential learning opportunities and leveraging industry partnerships, educational institutions can equip students with the skills and knowledge needed to thrive in an ever-evolving industrial landscape. Through proactive engagement and a shared commitment to excellence, industry and education can collectively succeed in fostering new generations of engineers and manufacturers.

To learn more about Siemens secondary school resources available to educators, visit Siemens. 

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In 2024, simulation tools are widely accepted as foundational tools for industrial innovation and are being widely deployed to solve the grand challenges that humanity is currently facing. With the accelerating growth in computational software and hardware capabilities, simulation has become ubiquitous in the design and development of new products.

Computations that took a week on the first personal computers back in the late 1970s can now be completed in milliseconds on a consumer-level laptop. Software developments have also massively accelerated the pace at which simulation takes place.

However, simulation technology in its current state has matured and hit a scalability ceiling. We have transitioned from a paradigm of computational limitations, where access to appropriate compute resources was a major hurdle (and manual efforts were negligible), to one where human expertise is becoming a scarce commodity. Accessibility to experts able to operate complex simulation software is the bottleneck.

Broadening the applications of simulation – offering anyone access to simulation technology and enabling simulation to be utilized in all phases of a product’s lifetime – is critical to surpassing today’s growth limitations. Substantial innovation in technology is required. Future innovations need to go beyond just improving accuracy and speed. They will need to lower the complexity, a major roadblock in the simulation industry.

AI, machine learning (ML) and large language models (LLMs) automation present new and exciting opportunities to democratize simulation. They could enable everyone who needs to perform a simulation to benefit from it. This shift holds the promise of significantly transforming the industry. Broadening the impact of simulation technology will allow the unlocking of novel markets. For example, marketandmarkets.com projects the size of the Digital Twin market to grow at a CAGR of more than 60% to $100 billion-plus in 2028. Similarly, the industrial metaverse is expected to be the same size and grow at the same rate.

The industrial metaverse and generative AI can democratize simulation, making it accessible to a much broader audience. To make this possible, future design environments will need to offer an experience as engaging and interactive as a video game, while keeping their industrial quality. Current barriers such as user experience and cloud computing access are being actively addressed and will enable us to rethink current approaches to democratization.

By maintaining continuous synchronization with its real-world counterparts, digital twins aim to integrate the real and digital with the ambition to enhance products, services and more. This is just the beginning. The industrial metaverse can act as a place where people and AI synergistically collaborate to create industrial innovations that solve real world problems and create further opportunities.

The ideal future scenario is one where designs and engineering data are constantly analyzed in the background. These analyses then keep designers and engineers continuously informed and systems optimized. Workflows are not only seamless but also highly automated — further unifying the virtual and physical worlds. Thereby designers will only partially be aware of the predictive technology in the background.

In the coming years, simulation will become relevant to a wider set of user profiles, be applicable to a greater number of use cases and be present throughout the entire lifecycle of products and industrial processes.

(Image: Siemens.)

That all being said, the six main trends in the future of simulation are:

1. Simulation is shifting left: Simulation will increasingly be applied at the early stages of product design, enhancing decision-making and cost-efficiency. With advancements in computational speeds, simulation will serve as a crucial tool in early design ideation, allowing non-expert users such as designers and sales professionals to perform preliminary assessments and increase return on investment.
2. Simulation is shifting right: Simulation will extend its reach into manufacturing and operational phases, bolstered by the rise of IIoT. The capability to simulate intricate production processes and customized products will become integral for operational efficiency. Autonomous simulations will be central in asset management, with digital twins using virtual sensors to augment limited physical data.
3. Simulation is shifting down: There will be a radical democratization of simulation, opening up its benefits beyond specialized engineers to a wider audience. User-friendly interfaces will cater to SMEs, hobbyists and the general populace, stimulating innovation and market expansion. This shift will see simulation tools becoming more accessible, resulting in a substantial increase in user base.
4. Simulation is shifting up: The future of simulation is autonomous and omnipresent within the Industrial Metaverse and Generative AI frameworks. Cloud-based simulation microservices will facilitate the shift from human-dependent simulations to those that are self-evolving, minimizing the need for human input and maximizing efficiency and adaptability.
5. Simulation is going deeper: Simulation tools will delve into higher resolution, enabling the modeling of systems across a broad spectrum of scales, from planetary to sub-molecular levels. This deep simulation capability will unlock new potential in advanced material design and other fields, pushing the envelope of efficiency and design precision.
6. Simulation will be more complete: The growing intricacy of systems-of-systems will drive the shift towards more agile and collaborative engineering approaches. Model-based systems engineering (MBSE) and systems modeling language (SysML) standards will become more prevalent, as reduced order models (ROMs) derived from detailed 3D models become crucial for rapid prototyping and system analysis. This will streamline the entire development process from concept to completion.

Put simply, in the very near future, engineering simulation will be used to aid in the design, engineering, manufacture and operation of products or processes. Anyone who has the need will easily be able to predict all relevant behavior. The solutions will be achieved either in a prescribed timeframe (from real-time to overnight) to an indicated level of accuracy, or to a prescribed accuracy (from good enough to certifiable) as fast as possible.

And eventually, tools will be aided by AI, ML and LLM embedded via the digital twin and industrial metaverse. Thus, everyone will use simulation either consciously or unconsciously.

Potential users of simulation technology have steadily increased since the 1950s, where it was used mostly by researchers and scientists. Ultimately, everyone will use simulation in the form of digital twins.(Image: Siemens.)

Backed by a global team of engineering specialists, Simcenter provides our customers with insight to the real-world performance of their products or process, allows them to accelerate innovation over the entire lifecycle and build a better tomorrow, faster. We are committed with our simulation and test solutions, whether software, hardware or services, to help industries have a positive impact on our world: improve how we live, how we travel, how we connect to each other and how we are cured.

To learn more, visit Siemens Simcenter.

About the Author

Jean-Claude Ercolanelli is Senior Vice President of Simulation and Tests Solutions for Siemens Digital Industries Software, a business unit of Siemens Digital Industries. He also serves as CEO of Siemens Industry Software NV. He and his team are responsible for developing and delivering Simcenter, a flexible, open and scalable portfolio of predictive simulation and test applications. Jean-Claude has more than 30 years experience in the computational aided engineering industry. He held a series of roles with increasing responsibility in business development and product management, beginning with Framasoft, then ESI group and later Ansys to eventually join CD-adapco, a company acquired by Siemens in 2016. He holds an Advanced Management Program degree from INSEAD and graduated from INSA Lyon, FR with a degree in Mechanical Engineering. He and his wife Christine reside in Lyon, France and have two adult children.

The post What is the future of engineering simulation? appeared first on Engineering.com.

Establishing transparency is key to making a sustainable battery industry. (Image: Siemens.)

Electrification is happening in industries around the world as manufacturers move away from non-renewable resources. A large portion of this strategy relies on the electrical storage potential of batteries. Electric vehicles (EVs) specifically have marked a significant shift for one of the most impactful industries in regard to carbon emissions: automotive and transportation. Rapid growth in battery EVs globally, concerns over ethical sourcing of critical minerals for batteries and a necessity to increase the use of recycled material for cell production are some of the key reasons driving a need for increased transparency and traceability across the complete battery supply chain.

Mounting government transparency regulations are also playing a pivotal role in creating traceability and transparency across the battery supply chain by requiring companies to deliver digital battery passports. EU regulators have mandated that by February 2027 any battery with more than 2 kWh capacity for industrial or EV applications must have a digital battery passport. These passports must contain information about material composition, sourcing location, carbon footprint, performance and lifecycle.

Digital battery passport efforts are gaining traction in other countries, as well. China, which accounted for 77% of global battery production in 2022 and 60% of all EVs sold in the world in 2023, has launched an initiative to develop a Chinese battery passport following EU regulations. Their aim is to facilitate trade with the EU by establishing similar data transparency requirements along the EV battery value chain in China, including carbon footprint, circularity and ESG. Similar regulations are being worked on in the U.S.

What the battery passport means to a growing industry

Battery passport requirements bring a new level of reporting to every facet of the battery value chain, from raw materials to finished products. Businesses will need to collect many different data types and do so in a transferable, verifiable and secure manner. This creates a challenge for battery manufacturers and end system integrators such as automakers to bring their products to market. While the end system integrators are the responsible party for providing the data that goes into a battery’s passport, much of the information comes from the broader value chain. Some of the simpler requirements might be easy to obtain from suppliers, such as the unique battery identifier, product information, country of origin, battery type and the battery model. But much more needs to be accessible via the battery passport identifier.

The battery manufacturer will need to provide information on the battery composition, recycled content percentages, the contents of critical raw materials and more. Meanwhile, data from the manufacturer’s value chain will be required to inform compliance certification and labeling, the total carbon footprint of the battery and information on responsible sourcing. In addition, battery performance over its lifecycle is another key data point for battery passports and will require the reporting of data collected through the battery management system. Reporting standardization will be critical in meeting the data requirements for battery passports as well as tracking the history of a battery as it is inspected, serviced or passed on to another party.

Making all of this happen efficiently and without risking IP will require a robust and secure digital platform and reliable partners.

Being able to track the composition of recycled materials will be critical to sustainability requirements. (Image: Getty Images/Bloomberg Creative.)

Accelerating an industry shift with digitalization

While battery passports are a new and unique requirement for industry, the components making it possible are already part of the Siemens toolset and expertise. In general, there are three major challenge areas to integrating battery passport requirements: the technical realm, the business realm and global cooperation.

On the technical side, Siemens is uniquely positioned to support the battery supply chain to meet battery passport regulations with battery passport software, along with the digital framework that empowers companies to collect and report the needed data in an efficient way. Siemens is working with customers, partners and industry experts to create a cloud-based, secure battery passport offering. In addition, a key challenge for automakers, battery suppliers and the rest of the supply chain is to have efficient availability of data to feed into a battery passport.

With its digital solutions, Siemens is ideally suited to empower the industry. Siemens’ PLM solution offers the single source of truth for batteries as well as for factories and facilitating seamless supplier collaboration. The data needed for battery chemistry, material composition and design for battery passport can be easily extracted from the digital twin of the product and manufacturing process. With Siemens’ MES solution bringing seamless IT/OT integration, companies can easily account for the data from the production floor to the battery passport. In addition, Siemens delivers end-to-end product carbon footprint data collection from the shop floor as well as the complete supply chain.

With its digital twin solutions for batteries, Siemens is striving to deliver new digital services. For example, battery state of health assessment, prognostication of issues to schedule servicing, optimized battery recycling guidelines and second life assessment leverage data from battery passport and digital twin solutions.   

Tools for the business processes associated with battery manufacturing are also needed for the battery passport system to function effectively. Businesses need to be able to conduct due diligence when working with suppliers, with reporting from the wider battery industry. Businesses need to be able to research the circularity practices, resource efficiency of different suppliers and even material recyclers to create a more sustainable business. And all the data being generated on the design and production side must be shared securely so that other parties can understand the characteristics of a battery through its battery passport.

The wide-reaching cooperation required by battery passport regulations puts pressure on creating strategic partnerships across the industry and complete value chain. Communicating the impacts of design choices, manufacturing operations and supplier selections is critical for accurate battery passports. To help customers with this evolution in transparency and communication, Siemens is working with leading industry consortiums such as Catena-X and the Global Battery Alliance.

Making a more sustainable battery business

The Siemens Battery Passport is a critical step in achieving transparency and sustainability for the battery value chain. It can radically transform the industry, ensuring ethical sourcing and sustainable practices. But that promise is not without challenges, including the need for strategic actions and collaborations among many diverse stakeholders. With government regulations, industry commitment and technological advancements playing a crucial role, the Siemens Battery Passport is set to redefine the future of the EV battery industry. And Siemens is a partner in making that a reality, with its expertise in technology solutions and understanding of the industry’s requirements.

About the author:

Puneet Sinha is Senior Director of the Battery Industry for Siemens Digital Industries Software. In this role, he heads the company’s strategy and cross-functional growth focus for batteries. Sinha has 15 years of industrial experience in battery and electric vehicles go-to-market strategy, product development and taking pre-revenue startups to successful exit. Prior to joining Siemens, Sinha worked at General Motors where he led global R&D teams to solve a wide range of issues with fuel cells and battery electric vehicles, and at Saft, a Li-ion battery manufacturer. He also served as VP of Business Development for EC Power, a Li-ion battery software and technology development startup. Sinha has a PhD in Mechanical Engineering from Pennsylvania State University. He has authored more than 20 highly-cited journal articles and been awarded seven patents on battery and fuel cells system design and operational strategies.

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