<table style="width: 100%;"><tbody><tr><td style="width: 100%;"><h2>Accelerating product design with simulation&nbsp;</h2><p dir="ltr">When applied early in the development cycle, simulation (CAE, FEA) technologies can rapidly accelerate the creation of high-performance, profitable products. However, choosing and implementing the right simulation solution effectively can be challenging when you have limited resources.</p><p dir="ltr">If you are considering implementing simulation (CAE, FEA) for the first time or utilize CAD embedded simulation tools but are running into challenges with speed and accuracy or want to take the next step and explore multi-physics analysis, the free virtual event Simulate at the Speed of Design event on &nbsp;May 16, 2024, maybe for you.</p><p dir="ltr">Learn from your peers and Altair’s experts how a mixture of software, support, and know-how can help you be more efficient and effective in developing new products.</p><p dir="ltr"><a href="https://wevlv.co/altair_simulate_2024" target="_blank" rel="noopener noreferrer" class="button-main-action">Register now.</a></p></td></tr></tbody></table><p dir="ltr"><em>This is an excerpt from a presentation by Keshav Sundaresh, Manager, Business Development of Altair. The full presentation, “The TRICK to Digital Twin Technology Adoption” can be attended at the <a href="https://wevlv.co/altair_simulate_2024" target="_blank" rel="noopener noreferrer">Simulate at the Speed of Design</a> event on May 16, 2024.</em></p><p>There is a lot of hype around digital twins. Still, for many years, the ability to actually execute them in a way that contributed to streamlining business through data-backed insights has been limited. Below, Keshav Sundaresh, the Global Director of Product Management – Digital Twin and Digital Thread from Altair, shares his insights into using the TRICK methodology to help separate true digital twin technology from fancy-looking interactive CAD models, which are not digital twins but rather mere digital shadows.&nbsp;</p><h2 dir="ltr">Simulation all around you</h2><p dir="ltr">Look around you. The device you’re reading this on right now is one of many machines, devices, and tools that surround you and support your everyday life – at work and outside. At Altair, we say our technology is always around you – even when you’re not conscious of it – keeping you safe, connected, and empowering you to thrive in today’s fast-paced world.&nbsp;</p><p dir="ltr">But what, exactly, do we mean by that? How is our technology integrated into both consumer goods like smart electronic devices, vehicles, and light fixtures as well as industrial objects like assembly lines, sensors, and wind turbines? The answer is digital twin technology, which has a powerful, wide-ranging impact in business and beyond. What can digital twin capabilities mean for your business?</p><p dir="ltr"><strong>Digital twin capabilities:</strong></p><ol><li dir="ltr"><p dir="ltr">Drive operational efficiency</p></li><li dir="ltr"><p dir="ltr">Facilitate a model-based ecosystem, providing access to authoritative data and knowledge for faster, better decision-making</p></li><li dir="ltr"><p dir="ltr">Drive superior engineering quality and product development performance</p></li><li dir="ltr"><p dir="ltr">Flatten the disruption curve for production</p></li></ol><p dir="ltr">For industrial companies, learning how to use this technology to streamline your production process could be such a changing move for the business. Especially in today’s world where our resources seem to have infinite uses, being able to simulate your production using technologies such as the CAE and FEA technologies are vital. This is where Altair’s Simulate at the Speed of Design event comes in. Coming up on the 16th of May, 2024, this is a must-attend for small and medium organizations looking to reduce the risk and cost of making late design manufacturing changes.</p><h2 dir="ltr">Digital Twin Took Center Stage at CES 2024</h2><p dir="ltr">Altair has been talking about digital twins for years. But it’s not just Altair and myself that believe in digital twins – my recent experience at CES 2024 was more than enough proof of that. At this year’s event, digital twin-related booths, presentations, and demonstrations were ubiquitous. It seemed like every organization – no matter their specialty, industry, or size – was talking about digital twin capabilities and showcasing what they were doing (or planned to do) with the technology.&nbsp;</p><p dir="ltr">Five years ago, a scant few were talking about digital twin technology, let alone creating it. The technology just wasn’t there; people liked the concept of digital twin, but the reality was Frankenstein-ish. Very few companies then had the open architecture-based, flexible, and purpose-driven workflows necessary to implement digital twin capabilities. But now digital twin is firmly in the spotlight.</p><p dir="ltr"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEzODc0NDQ3NjI3LXRyaWNrIDIuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"></p><p dir="ltr" id="isPasted">Today, the prevalence of accessible, intuitive data and high-performance computing (HPC) environments – like Altair’s – has changed the digital twin adoption landscape. Now, instead of a hodgepodge of solutions and environments, digital twin is more akin to a living and breathing dynamic system with a single, unbroken thread.</p><p dir="ltr">Digital twin capabilities have also boomed because people (executives in particular) are finally seeing their true business value. They concretely understand that digital twin can transform their business and propel them into a streamlined, digitalized future. It’s safe to say that these organizations wouldn’t be putting the time, effort, and creativity into showcasing digital twin capabilities at events like CES if they weren’t betting big.&nbsp;</p><p dir="ltr">However, there are still plenty of organizations – too many, in my opinion – that don’t understand what digital twin technology is and how they can start harnessing its power. Luckily, there’s a TRICK to digital twin – literally.</p><h2 dir="ltr">Why "TRICK"?</h2><p dir="ltr">“TRICK” is a simple, compelling acronym to serve you on your digital twin journey. TRICK is neither sequential nor mutually exclusive. It’s merely a framework to help separate true digital twin technology from fancy-looking interactive CAD models, which are not digital twins but rather mere digital shadows. Without further ado, let’s explore the TRICK to digital twin technology.&nbsp;</p><p dir="ltr"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEzODc0NDc1MDEyLXRyaWNrLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib"></p><h3 dir="ltr" id="isPasted">T: Think in Systems</h3><p dir="ltr">Digital twin technology is defined – literally and metaphorically – by its function as a system of systems. Digital twin technology is not merely the sum of its parts, a collection of a bunch of components, processes, and systems. Rather, it’s the interaction of all these things acting as one in a holistic environment.&nbsp;</p><p dir="ltr">Think of it this way: Imagine you’re tasked with building the best car in the world. To accomplish this, you can mix and match components and systems from any kind of car in the world. Maybe you want your car to have the body of a Maserati, the propulsion system of a Tesla, the infotainment console of a GMC, the steering of a Mercedes-Benz, the seats of a Lexus, and so on. If you threw all these disparate systems into a single car, do you think you’d have the “ultimate” car? You wouldn’t. Why not? Because these sophisticated mechanisms aren’t like Legos that you can just slot in at will, regardless of the pieces’ context. These are complex systems designed to function on their own and as part of a specific, holistic system – that holistic system being a vehicle model, in this case. This same principle applies to wind turbines, semiconductor capital equipment, aircraft, and so on.&nbsp;</p><p dir="ltr">Digital twin technology enables us to better understand and improve interactions between components and how they function. Everything lives in the spaces where systems interact. Engineers and designers should always be thinking at this level to minimize issues and maximize efficiency.&nbsp;</p><p dir="ltr">Customer evidence: <a href="https://altair.com/resource/getting-the-signal-with-digital-twin-leonardo-deploys-digital-twin-to-capture-in-flight-behavior-of-helicopter-radar?lang=en" target="_blank">“Leonardo Deploys Digital Twin to Capture In-Flight Behavior of Helicopter Radar”&nbsp;</a></p><h3 dir="ltr">R: Reduced-Order Modeling&nbsp;</h3><p dir="ltr">When we want to share insights broadly, we want things to be as simple and actionable as possible. Real-life processes are often incredibly complex and hard to distill into a model or equation. But the beauty of mathematics is that it allows us to abstract, simplify, and standardize real-world knowledge. This is the goal of <a href="https://altair.com/romai" target="_blank">reduced-order modeling</a> (ROM).&nbsp;</p><p dir="ltr">For people to scale their insights and unlock digital twin breakthroughs, they can’t be bogged down in messy, voluminous data. ROM helps reduce the computational complexity of models in physics-based or statistical simulations. Some of our solutions in <a href="https://altair.com/products/platforms/altair-hyperworks" target="_blank">Altair® HyperWorks®</a>, our design and simulation platform – such as <a href="https://altair.com/physicsai" target="_blank">Altair® physicsAI™</a> and <a href="https://altair.com/romai" target="_blank">Altair® romAI™</a> – specialize in ROM within physics-based and test environments. And <a href="https://altair.com/altair-rapidminer" target="_blank">Altair® RapidMiner®</a>, our data analytics and AI platform, offers targeted, powerful capabilities for <a href="https://altair.com/data-transformation" target="_blank">all other kinds of data.</a> But these are just two sides of the same coin.&nbsp;</p><h3 dir="ltr">Importance of Explainable, “White Box” Solutions</h3><p dir="ltr">No matter how you approach ROM, you should always be using explainable, transparent solutions, otherwise known as “white box” solutions. White box solutions allow you to see exactly how AI-based models and algorithms are working, including:</p><ul><li dir="ltr"><p dir="ltr">Metadata sources</p></li><li dir="ltr"><p dir="ltr">What datasets were used</p></li><li dir="ltr"><p dir="ltr">What underlying equations were used</p></li></ul><p dir="ltr">This approach gives you greater insight and makes it clear to everyone throughout development how models are functioning and why. This is compared to “black box” solutions, which offer AI capabilities but are fully hidden from users – unexplainable, in other words.</p><p dir="ltr">Customer evidence: <a href="https://altair.com/resource/leveraging-ai-to-improve-accuracy-in-a-real-time-hardware-application?lang=en" target="_blank">“Leveraging AI to Improve Accuracy in a Real-Time Hardware Application”</a></p><h3 dir="ltr">I: Integrate</h3><p dir="ltr">Digital twin approaches work best when all users – including people without specialized engineering and data skill sets and non-coders – can leverage advanced tools without needing to rely on experts or support teams. This requires formalizing and democratizing computational intelligence.&nbsp;</p><p dir="ltr">With integrated digital twin capabilities, teams can virtualize products, processes, and equipment by integrating the best available models informed by sensor and metrology data, guided by domain knowledge, bolstered with software and hardware compute, and continuously synchronized with physical counterparts.&nbsp;</p><p dir="ltr">Customer evidence: “<a href="https://altair.com/resource/rolling-out-a-better-process-digital-twin-slashes-waste-15-and-runtime-hours-to-seconds?lang=en" target="_blank">Digital Twin Slashes Waste 15% and Runtime Hours to Seconds</a>”</p><h3 dir="ltr">C: Convergence is Key</h3><p dir="ltr">Digital twin is a living, breathing model of systems that is grounded in physics assimilating data from inspections, sensors, etc. These systems can be physical, biological, or business process-related. Developing and sustaining a digital twin ecosystem that connects the infrastructure, environment, and methodology (tools, methods, and processes) requires the convergence of simulation, HPC, <a href="https://altair.com/internet-of-things-applications" target="_blank">Internet of Things</a> (IoT), and AI.&nbsp;</p><p dir="ltr">Being able to offer this in a single, unified environment is what has helped Altair deliver the market’s <a href="https://altair.com/one-total-twin" target="_blank">premier digital twin offering</a>. Regardless of what industry you’re in or what product or system you want to model, we provide one total digital twin solution that optimizes workflows from concept to real-time in-service operation.</p><p dir="ltr">Customer evidence: “<a href="https://altair.com/resource/harnessing-the-power-of-big-data-ai-and-simulation-to-accelerate-product-innovation?lang=en" target="_blank">Harnessing the Power of Big Data, AI and Simulation to Accelerate Product Innovation</a>” and “<a href="https://altair.com/resource/developing-sustainability-with-ai?lang=en" target="_blank">Developing Sustainability with AI</a>”&nbsp;</p><h3 dir="ltr">K: Knowledge-Driven Workflows</h3><p dir="ltr">Speaking of workflows, that brings us to TRICK’s final letter. As teams progress through the development and testing cycle, they generate a ton of knowledge at each step. But often, they’re losing a lot of knowledge and experience in the spaces between each step because information is trapped in silos. These silos act like a filter that sifts valuable nuggets of insight from between each phase until hardly any is left at the end of the line.</p><p dir="ltr">When I say digital twin means creating “knowledge-driven workflows,” I mean that it can capture information and experiences into a model-based ecosystem that can then be used to develop traceability and governance and improve product quality. A model-based systems engineering workflow breaks horizontal silos from requirements to product end of life by providing a model as a common language of communication.&nbsp;</p><p dir="ltr">These knowledge-driven workflows reduce design errors once you finally get to the product testing and release stages. There’s no such thing as a universal mistake-proof workflow, but you can certainly minimize errors and the time spent correcting them by harnessing previously lost knowledge.&nbsp;</p><p dir="ltr">As the maxim goes, life isn’t so much about being right as it is about being less and less wrong. Digital twin technology gives people a transformational way to innovate, become more efficient, and develop the next generation of standout products. Digital twin is the best way to be less wrong – and continuing to be less wrong is the real “TRICK” to unlocking innovation.&nbsp;</p><p dir="ltr">To learn more about how Altair is enabling digital twin technology, register to attend the Simulate at the Speed of Design 2024 event, where Keshav will present the TRICK methodology in more detail. You’ll also be able to attend ‘simulation starter;’ events, designed for those who do not currently use simulation technologies, are outsourcing most/all of their analysis, or still use limited CAD embedded simulation tools.&nbsp;</p><p dir="ltr">In these sessions you can understand the benefits of the technologies from companies that have recently implemented simulation using Altair SimSolid; and through interactive sessions, experience how easy it is to get started, even with limited resources or on the Cloud.</p><p dir="ltr"><a href="https://events.altair.com/simulate-at-the-speed-of-design-2024/#:~:text=Following%20on%20from%20Altair's%20highly,their%20development%20process,%20developing%20more" target="_blank" rel="noopener noreferrer" class="button-main-action">Read more about the event&nbsp;</a><a href="https://wevlv.co/altair_simulate_2024" target="_blank" rel="noopener noreferrer">here.</a></p><p dir="ltr"><em>This article was contributed to by Philemon Mayowa.</em></p>

“TRICK” is a simple, compelling acronym that serves you on your digital twin journey. It’s a framework to help separate true digital twin technology from fancy-looking interactive CAD models, which are not digital twins but rather mere digital shadows. 

The TRICK to Digital Twin Technology Adoption

<p id="isPasted">All objects consist of atoms, which are made up of protons and neutrons in the atomic nucleus, surrounded by electrons. Once one enters this universe of the smallest things – the quantum world – it all gets very complicated very quickly. When researchers want to know exactly how atoms are connected and form solid objects, they come up against limits. No computer is powerful enough to calculate material properties precisely when many different atoms are involved. A researcher from the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt; DLR) has now investigated a way to do this via digital quantum simulation. This requires a quantum computer with initially only a few functioning computing and memory units, qubits, which could already answer questions about materials research – using quantum computing to ultimately even improve itself.</p><p>"In contrast to problems that can be solved with increased computing power alone, the complexity of quantum systems exceeds the capabilities of all supercomputers available today, combined. This means that we are currently unable to find explanations for phenomena from the quantum world, including conductivity, magnetism and superconductivity," says Benedikt Fauseweh from the <a href="https://www.dlr.de/en/sc" target="_blank">DLR Institute for Software Technology</a>. "Quantum physics is fundamental to understanding our world, especially solid-state physics." In solids, atoms or molecules are so close together that they hardly move at all. Metals, semiconductors, ceramics and many plastics are solids.</p><h2>A visionary idea – but quantum computers still make mistakes</h2><p>The concept of simulating quantum phenomena on a quantum computer is more than 40 years old. In 1982, the US-American physicist Richard Feynman (1918 to 1988) proposed using the principles of quantum mechanics itself to simulate the behaviour of quantum systems. “The development of modern quantum computers is bringing Feynman’s visionary idea closer to reality. Of course, it remains the dream of all physicists to describe all of nature in its full complexity,” says Fauseweh. But we are not there yet; quantum computers still make errors in complex calculations because their qubits (computing and storage units) are not stable or cannot be controlled, leading to incorrect results. Nevertheless, researchers are looking for ways to run sophisticated simulations on quantum computers.</p><h2>Large number of qubits not required</h2><p>Fauseweh has described this in the scientific journal <a href="https://www.nature.com/articles/s41467-024-46402-9" target="_blank" rel="noopener noreferrer">Nature Communications</a> and included worldwide research results. His analysis shows that a large number of qubits is not necessarily required for the simulation of complex quantum systems: "If we use too many qubits, the results are no longer useful due to errors in the quantum computer," explains Fauseweh. "We have to identify precisely those applications that still deliver a correct result even with errors in the quantum computer." So, with quality before quantity, digital quantum simulations could soon solve the mysteries of solid-state research: why do interactions between many particles prevent their movement in solids? Why, for example, do some materials not heat up as expected in a magnetic field? How do materials change their properties when they are irradiated with lasers? "It is also exciting that we can use quantum computers in materials research to make quantum computers themselves better."</p><p>Different technologies for quantum computers certainly complement one another.<a href="https://www.dlr.de/en/latest/news/2022/03/computing-with-light" target="_blank">&nbsp;Photonic quantum computers</a> or quantum computers with <a href="https://www.dlr.de/en/latest/news/2023/02/atomic-shells-become-computational-building-blocks" target="_blank">neutral atoms</a>, for example, would be particularly suitable for some applications, while quantum computers with<a href="https://www.dlr.de/en/latest/news/2022/04/contract-awarded-as-part-of-the-dlr-quantum-computing-initiative" target="_blank">&nbsp;ion traps</a>, with <a href="https://www.dlr.de/en/latest/news/2022/04/how_diamonds_become_qubits" target="_blank">nitrogen vacancies in diamond</a> or other <a href="https://www.dlr.de/en/latest/news/2023/04/quantum-computers-out-of-the-lab-into-the-world" target="_blank">solid-state spins</a>, would suit others. These hardware technologies are being developed in the <a href="https://www.dlr.de/en/research-and-transfer/featured-topics/quantum-technologies/the-dlr-quantum-computing-initiative-qci" target="_blank">DLR Quantum Computing Initiative (DLR QCI)</a>.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 768px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712996854792-image-1920-0e0e1372dc051ff5fc2e347f9452228c.jpeg" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner"><strong>Goal – improving materials.</strong> Quantum computers can simulate solid-state structures that even the best supercomputers cannot calculate. They can therefore help to improve materials for high-tech applications such as photovoltaic systems. Credit: © DLR. All rights reserved</span></span></span></p><h2 id="isPasted">Analogue simulations reach their limit</h2><p>In recent decades, researchers have used analogue quantum simulations to assist them – with a quantum platform that serves as a substitute for the actual system. The behaviour of the substitute quantum platform is adjusted so that it corresponds to that of the system under investigation. In this way, it is possible to simulate quantum systems that supercomputers had already failed to – and with this the advantages of quantum simulation become apparent. Analogue quantum simulation has been a common method until now. "However, it has the disadvantage that these machines are not flexible. They can only do one type of simulation," says Fauseweh. "What we want is the universality of a digital quantum computer. That is the most important advantage we expect from the different approaches."</p>

Crystal lattice of a solid (symbolic image). When researchers want to know exactly how atoms are connected and form solid objects, they come up against limits. Digital quantum simulations could soon solve the mysteries of solid-state research. Credit: stock.adobe.com

Digital quantum simulations can be used to solve problems in solid-state research, for example conductivity, magnetism or superconductivity.

Quality matters - Advances in digital quantum simulation

<p id="isPasted">In an industry where speed, accuracy, and cost-effectiveness are crucial, <a href="https://yawmanflight.com/" target="_blank">Yawman Flight</a> teamed up with <a href="https://motocilino.com/" target="_blank">MotoCilino Engineering Service</a> firm and Nexa3D to address the challenge of designing and developing a first-of-its-kind handheld controller for flight simulations.The Yawman Arrow is the fusion of a traditional handheld gaming controller and the unique flight controls found on aircraft of all sizes. Designed for precision and versatility, the Arrow brings together a traditional joystick, desktop throttle quadrant, and rudder pedals – and more – all into a handheld form factor.</p><p>The result is a breakthrough in both product innovation and manufacturing efficiency, with <a href="https://nexa3d.com/3d-printers/xip/" target="_blank">XiP Desktop 3D Printer</a> playing a pivotal role in achieving success.</p><h2 id="isPasted">Challenge</h2><h5>Create high-fidelity prototypes quickly and cost-effectively without investing in tooling early on in the development process</h5><p><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712135865489-creating-high-fidelity-prototype-768x720.jpg" style="width: 100%px;" class="fr-fic fr-dib"></p><p>Yawman Flight faced the challenge of creating a unique flight simulator controller under significant time and budget constraints. The primary objectives were to develop a first-of-its-kind handheld flight simulator controller and complete the project rapidly and cost-effectively, without investing in costly injection molding tooling. Additionally, the team required high-quality surface finish and mechanical performance of all of the components so achieving injection molding-like quality quickly and affordably were critical to the success of the project.</p><p>Yawman Flight’s intended customer base is flight simulation enthusiasts and professionals in the aviation industry who demand high-quality, realistic flight simulation experiences. The flight simulation industry requires high-quality products that can meet strict specifications for part tolerances and mechanical performance in order to provide a truly realistic simulation experience.</p><blockquote id="isPasted"><p>“The XiP allowed us to create a final product that exhibits injection molded like qualities in terms of surface finish and performance, but without the costs and long lead times. We would’ve been delayed by as much as a year had we used another manufacturing method during the prototyping phase.”<strong>Thomas Nield, Engineering Lead at Yawman Flight</strong></p></blockquote><p><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712135927444-1712135927444.png" class="fr-fic fr-dib"></p><h2 id="isPasted">Solution</h2><h5>The XiP proved to be ideal tool for the job due to its accuracy and print speed</h5><p><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712135943136-flight-controller-3D-printed-XiP-768x769.jpg" style="width: 100%px;" class="fr-fic fr-dib"></p><p>Yawman Flight collaborated with MotoCilino Engineering Service firm to develop a unique controller in record time, going through 17 different design iterations before validating the final assembly in just a few weeks. The final prototype controller consists of 36 different components, all of which were printed on the <a href="https://nexa3d.com/3d-printers/xip/" target="_blank">XiP desktop 3D printer</a> using xABS3843 high-performance resin.</p><p>The team at MotoCilino chose to print the components on the XiP due to printer’s reliability and high print quality using <a href="https://nexa3d.com/materials-selection-tool/" target="_blank">performance grade resin</a> that mimic mechanical properties of final production grade ABS. In fact, the finished controller visual aesthetic and mechanical performance was virtually indistinguishable from those components that were molded. “Printing on the XiP allowed us to deliver components to the Yawman team in half the time it would’ve taken us had we used another desktop SLA printer, and the print quality on the XiP is just superior,” said Paul Cilino, Owner of Motocilino.</p><h2 id="isPasted">Benefit</h2><h5>Accelerated time to market by nearly a year and delivered high-fidelity prototypes at a fraction of the cost</h5><p><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1712135957381-36-piece-3D-printed-flight-controller-768x774+%281%29.jpg" style="width: 100%px;" class="fr-fic fr-dib"></p><p>The team was able to finalize the design and deliver a high quality prototype with injection molding-like surface finish and mechanical performance quickly and cost effectively. The finished assembly included over 36 printed components, all virtually indistinguishable from its injection molded counterparts in both finish and mechanical performance. As a result, the team is now ready to scale to production as they prepare for commercial launch in July, 2023, on the heels of tremendous initial demand received during its beta launch earlier in the year.</p>

Working with engineering experts at MotoCilino, the team commercialized a handheld flight controller in record time

Revolutionizing Flight Simulation with XiP Desktop 3D Printer

<h2 id="isPasted">Accelerating product design with simulation&nbsp;</h2><p>When applied early in the development cycle,<a href="https://www.wevolver.com/profile/altair" rel="noopener noreferrer" target="_blank">&nbsp;simulation (CAE, FEA) technologies</a> can rapidly accelerate the creation of high-performance, profitable products. However, choosing and implementing the right simulation solution effectively can be challenging when you have limited resources.</p><p>If you are considering implementing simulation (CAE, FEA) for the first time or utilize CAD embedded simulation tools but are running into challenges with speed and accuracy or want to take the next step and explore multi-physics analysis, the free virtual event <strong>Simulate at the Speed of Design</strong> event on &nbsp;<strong id="isPasted">May 16, 2024</strong><strong>,&nbsp;</strong>maybe for you.</p><p>Learn from your peers and Altair’s experts how a mixture of software, support, and know-how can help you be more efficient and effective in developing new products.</p><p><a href="https://wevlv.co/altair_simulate_2024" target="_blank" rel="noopener noreferrer" class="button-main-action">Register Now</a></p><h3>Why should you attend?</h3><p>Learn how small and medium organisations, are deploying Altair simulation (CAE, FEA) solutions such as Altair® Inspire™, SimSolid® and SimLab® to reduce the risk and cost of late design manufacturing changes.</p><h3><strong>Topics covered in the event</strong></h3><ul><li>Presentations from small-medium business (SMB) customers of Altair and Altair Channel Partners, on their transition from CAD embedded simulation and how they successfully implemented CAE, FEA simulation in their product development workflow.</li><li>Generative design, Concept development, topology optimization or design for manufacture in Altair® Inspire™.</li><li>Rapid structural analysis in Altair SimSolid® for parts or large assemblies.</li><li>Drop test, thermal, CFD, fatigue or combined multiphysics analysis in Altair® SimLab®</li><li>Manufacturing feasibility, Process simulation with Inspire Cast, Inspire Form, Inspire Mold, or Inspire Print3D.</li></ul><p>Sessions will feature live audio translation into English, French, German, Spanish, Italian, Portuguese, Japanese, Chinese, and Korean.&nbsp;</p><header><h3>Who should attend?</h3></header><ul><li>Individuals working in the design department, including engineers, design managers, and heads of the design department</li><li>If you are using 3D CAD and considering adopting simulation tools (CAE, FEA) for the first time</li><li>Anyone experiencing difficulties with the speed and accuracy of CAD-embedded simulation (CAE, FEA) tools</li><li>CAE Engineers, analysts, and the head of the CAE department</li><li>If you are interested in exploring multi-physics analysis and looking to take the next step</li><li>Managers of related departments who rely on the CAE department or outsource simulation services</li></ul><p><a href="https://wevlv.co/altair_simulate_2024" target="_blank" rel="noopener noreferrer" class="button-main-action">Register Now</a></p><h2 dir="ltr" id="isPasted">Event Schedule and Speaker Lineup</h2><p dir="ltr">Hear from a diverse lineup of industry experts who will share their insights and expertise on various aspects of product development and simulation technology. Following is a list of some of the notable speakers from the event.&nbsp;</p><h3 dir="ltr">Simulation at the Speed of Design Speakers:&nbsp;</h3><p dir="ltr"><strong>Chief Product and Strategy Officer, Altair, </strong></p><p dir="ltr">Ravi Kunju will talk about the power of simulation in speeding up the design process. Drawing upon his extensive experience and expertise in product development and strategy, Ravi will provide insights into how simulation technologies can transform the design process, enabling engineers and designers to iterate rapidly and optimize product performance.</p><p dir="ltr"><strong>Co-Director, Simulation-driven Systems Development Practice, CIMdata,</strong></p><p dir="ltr">Sandeepak Natu brings 27 years of experience in engineering simulation, entrepreneurship, and business management. He will share insights on advancing simulation adoption in SMEs and unlocking new avenues for growth and competitiveness.</p><h3 dir="ltr">Simulation Starters Speakers:</h3><p dir="ltr"><strong>Product Manager at Altair for SimSolid,</strong></p><p dir="ltr">Girish Mudigonda will be presenting on "SimSolid Cloud - Simulate Anywhere." He will discuss the capabilities of SimSolid Cloud, highlighting its versatility in enabling simulation from anywhere. Attendees can expect insights into the features, benefits, and practical applications of SimSolid Cloud, empowering them to conduct comprehensive simulations efficiently.</p><p dir="ltr"><strong>Engineering Manager at Modulift, UK,</strong></p><p dir="ltr">Harshal Kulkarni will share insights into how Altair SimSolid has slashed design development time by 70%. He will highlight Modulift's experience with FEA analysis and the transition to Altair SimSolid, emphasizing its user-friendly interface and efficiency benefits for engineers.</p><p dir="ltr"><strong>Senior Engineer at Daewonsanup,</strong></p><p dir="ltr">Kyungsoo Kim will address the challenges associated with squeak and rattle noise in vehicle seats and how they impact driver fatigue. He will discuss the analytical evaluation methods employed, including testing and CAE programs, to identify the root causes of these noises. Additionally, Kyungsoo will highlight the utilization of Altair SimSolid for efficient and accurate quantification of mechanical and electrical factors contributing to seat noise.&nbsp;</p><h3 dir="ltr">Simulation Driven Design Speakers:</h3><p dir="ltr"><strong>Director of GTT at Altair,&nbsp;</strong></p><p dir="ltr">Jaideep Bangal will lead the expert chat on "Accelerate Simulation-Driven Design with Altair Inspire." Drawing on his expertise, Jaideep will explore the capabilities of Altair Inspire for simulation-driven design, highlighting its role in accelerating the product development process. Attendees can expect insights into leveraging Inspire to optimize designs efficiently and effectively.</p><p dir="ltr"><strong>Representing Bionic MeshDesign,</strong></p><p dir="ltr">Thomas Spoida will discuss strategies for accelerated gigacasting development, focusing on overcoming challenges in large-scale castings. Attendees can expect insights into innovative approaches for efficiently designing and optimizing large castings to expedite time-to-market and enhance product quality.</p><h3 dir="ltr">Exploring Multiphysics Speakers:</h3><p dir="ltr"><strong>CAE Specialist Engineer at BeonD,</strong></p><p dir="ltr">Luigi Scrimieri will present "How BeonD identifies cell thermal runaway behavior with SimLab." He will demonstrate how computational simulations, juxtaposed with experimental data, accurately predict cell thermal runaway, paving the way for scalable simulations to modules and full battery packs.</p><p dir="ltr"><strong>SimLab Specialist, Altair,</strong></p><p dir="ltr">D V Siddharth and Senior Application Engineer, Bharadwaj Gundepudi will showcase the practical applications and capabilities of SimLab across various domains, including structures, thermal analysis, electronics, and motor simulations. Attendees can expect to gain insights into the versatility and effectiveness of SimLab in addressing diverse engineering challenges.</p><h3 dir="ltr">Manufacturing Simulation Speakers:</h3><p dir="ltr"><strong>Chief Product and Strategy Officer at Altair,</strong></p><p dir="ltr">Ravi Kunju will discuss the benefits of a Simulation-Driven Manufacturing (SDfM) approach, enabling product designers to detect design flaws earlier and optimize manufacturing processes. Attendees will gain valuable knowledge on leveraging simulation-driven design to streamline manufacturing processes and achieve optimal results.</p><p dir="ltr"><strong>Managing Director of Shiva ToolTech,</strong></p><p dir="ltr">Shivaji Pawar will present a case study on expediting the design and fabrication process of casting dies for automotive components cast with Aluminium AlSi7Mg via Gravity Tilt Pouring. Attendees will learn how the integration of Inspire Cast significantly reduced die development times, demonstrating its efficacy in streamlining the casting process.</p><p dir="ltr"><strong>CEO of Presskon Engineering GmbH,</strong></p><p dir="ltr">Gökhan Cetinkaya will present a case study illustrating the integration of Finite Element Method (FEM) simulation into their workflow. Gökhan will demonstrate how FEM simulation can assist design engineers in optimizing their workflows, reducing costs, and enhancing product quality.</p><p dir="ltr"><strong>Note:&nbsp;</strong>Please note that the event will be broadcast in two different time zones to accommodate attendees from various regions. Kindly refer to the respective time zone schedule to ensure you don't miss any sessions.&nbsp;</p><p dir="ltr">For the full list of speakers and presentation times, <a href="https://events.altair.com/simulate-at-the-speed-of-design-2024/#:~:text=Following%20on%20from%20Altair's%20highly,their%20development%20process,%20developing%20more" target="_blank">visit the event page.</a></p>

Learn how to reduce the risk and cost of late design, and manufacturing changes at the free Altair Virtual Global Event on May 16, 2024

Simulate at the Speed of Design 2024

Example output of DeepScenario’s AI software that detects and tracks all moving objects in a scene in a fully automated way. (DeepScenario) 

There are many divergent-but-related ideas about how best to leverage simulation to ‘teach’ and test automated vehicles. Global AV simulation experts continue to attack the technology’s persistent obstacles.

AV simulation testing faces a long and winding road

<p dir="ltr" id="isPasted">The field of robotic dexterous hands is rapidly evolving with technological advancements and innovative research. These agile devices have a potent potential to change industries and day-to-day life and so are the focus of research within the robotics industry.&nbsp;</p><p dir="ltr">However, the engineering challenges posed by dexterous robotic hands are numerous. Complexities like miniaturization, grip and sensing, accurate simulation testing as well as the application of AI and machine learning are hurdles that researchers must overcome to achieve robotic hands that are as capable as human hands.</p><p dir="ltr">In this article, we will discuss the complexities of dexterous hand development and offer some insights into how they are being solved using examples from <a href="https://www.wevolver.com/profile/shadow.robot" target="_blank" rel="noopener noreferrer">Shadow Robot</a>, a company known for creating one of the most advanced dexterous robotic hands in the world.&nbsp;</p><h1 dir="ltr">Complexities of Dexterous Robotic Hands</h1><p dir="ltr">Key areas of robotics development like miniaturization, sensing, and control are particularly complex when integrated with dexterous hands. Miniaturization is a challenge because dexterous hands must be capable of human-like precision and control, which is difficult to achieve in a compact space. Sensing is also a challenge because dexterous hands require a high degree of sensitivity in order to detect and respond to elements in their environment. Control poses yet another challenge, because dexterous hands require sophisticated algorithms and engineering to coordinate the movements of multiple fingers and joints<sup>[1]</sup>.</p><p dir="ltr">Addressing these challenges is important for broader adoption of dexterous hands in both research and industry.&nbsp;</p><p dir="ltr">Improving sensing and grasping methods opens new applications for robotic manipulation. When combined with AI and machine learning, dexterous robotic systems have the potential to apply human-like adaptation and movement to tasks that are repetitive, hazardous, or that require more speed or precision than a human can provide<sup>[2]</sup>.</p><h2 dir="ltr">Multi-force grip and sensing</h2><h3 dir="ltr">The challenge</h3><p dir="ltr">Dexterous robotic hands must be able to perform intricate tasks and manipulate different types of objects in variable environments. This means that dexterous hands need complex sensor and control systems and must incorporate multi-force gripping that can adapt according to the object and the task. Human grip force is controlled by biological systems, and to replicate this in robotic hands, researchers must develop sensors and control systems that can accurately measure and respond to features of the object and environment, like pressure, interaction forces, and vibrations<sup>[3]</sup>.</p><h3 dir="ltr">The solution</h3><p dir="ltr">Sensing capabilities play an important role in dexterous robotic hands. Visual and tactile feedback enables robots to quantify physical properties and is used increasingly in robotic systems. The growing availability of affordable tactile sensors has encouraged exploration of real-world tactile applications, such as the field of agriculture, where grip force is critical in applications like automated harvesting<sup>[4]</sup>.</p><p dir="ltr">Researchers have developed innovative sensors and control systems to enable dexterous manipulation of objects by robotic hands. Examples include:</p><ul><li dir="ltr"><p dir="ltr">MIT’s GelSight Svelte, a finger-shaped camera-based sensor that provides high-resolution tactile sensing over a large area, which enables the hand to perform different types of grasps, including pinch grasps. The sensor has a flexible backbone and uses mirrors to reflect light into a camera, which can see along the length of each finger.<sup>[5]</sup></p></li><li dir="ltr"><p dir="ltr">The ILDA (integrated linkage-driven dexterous anthropomorphic robotic hand), developed by researchers at Ajou University, with six-axis force and torque sensors that can measure the various forces acting on the robotic fingertips. Researchers integrated algorithms that can determine the contact force parameters, including the contact, location, and the triaxial force applied to the fingertips<sup>[6]</sup>.</p></li><li dir="ltr"><p dir="ltr">Shadow Robot’s Dexterous Hand, which has over 100 sensors that operate at up to 1 KHz, which give the hand multiple tactile sensing options and fast response. The hand can be integrated with tactile fingertips, which enable it to perform multiple grasps, like pinch grasps, lateral pinch grasps, and a power grasp that uses the entire sensing area of the fingers<sup>[7]</sup>.</p></li></ul><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAwNzMzNjQxMzgxLWltYWdlMS5qcGVnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 300px;" class="fr-fic fr-dib"><span class="fr-inner">The Shadow Dexterous Hand gripping a lightbulb. Source: shadowrobot.com</span></span></span></p><h2 dir="ltr" id="isPasted">AI and machine learning</h2><h3 dir="ltr">The challenge</h3><p dir="ltr">Artificial intelligence and machine learning play an important role in the development of dexterous robotic hands. When these technologies are applied to sensor data, they can improve perception, object recognition, grasping, and manipulation capabilities. However, deploying AI and ML in robotic hands is a challenge because it can be difficult to produce enough data for effective learning<sup>[8]</sup>.</p><h3 dir="ltr">The solution</h3><p dir="ltr">Shadow Robot has integrated AI and ML into their dexterous hand technology to manipulate physical objects and complete tasks in a way that is intuitive for the operator<sup>[9]</sup>. The company has worked with companies like OpenAI to advance research in this area. The Dexterous Hand provides abundant sensor and positional data that researchers can apply to machine learning algorithms and develop innovative control systems<sup>[10]</sup>.</p><p dir="ltr">Deep reinforcement learning has also emerged as a promising approach to control dexterous robotic hand systems. Deep reinforcement is model-agnostic, meaning that controls do not rely on a specific model of the system. To overcome the problem of the small sample size for learning data, researchers have combined deep reinforcement learning with human demonstration, which enables learning using sample sizes of just a few hours of robot learning<sup>[11]</sup>.</p><h1 dir="ltr">The role of simulation testing to overcome challenges</h1><p dir="ltr">The development of dexterous robotic hands is a complex and challenging task that requires substantial testing and iteration to achieve successful mimicry of the human hand. However, it can quickly become costly, and sometimes hazardous, to perform field testing with physical robots. Instead, simulation testing provides data points on the capabilities of robotic systems using a computer-based simulation of the hardware<sup>[12]</sup>.</p><p dir="ltr">Simulation testing is a practical, safer, and more scalable alternative to field testing and is critical to developing human-like functionality in dexterous hands<sup>[13]</sup>. Using simulation testing, developers can evaluate and compare the performance of robotic hands while adjusting design characteristics and environmental factors. Researchers can perform stress tests on the system in less time and without damaging physical hardware or putting the safety of humans at risk<sup>[14]</sup>.</p><p dir="ltr">However, creating realistic simulations for dexterous robotic hands poses another set of challenges. One of the biggest challenges of simulation testing is to create a simulation that is realistic enough to be useful. If the simulation is unable to replicate the kind of environment that the robot will operate in and adapt to, then the data from the simulation cannot reliably be used to improve the robot<sup>[15]</sup>.</p><p dir="ltr">Another challenge of simulation testing is the lack of reproducibility and the resource costs associated with simulators<sup>[16]</sup>. Despite these challenges, simulation testing is critical to the ongoing development of dexterous robotic hands. It allows developers to test and evaluate new algorithms and models in a controlled environment before building physical systems<sup>[17]</sup>.</p><h2 dir="ltr">Shadow Robot’s vision and simulation testing</h2><p dir="ltr">Simulation testing is a key part of how Shadow Robot is overcoming major challenges in dexterous hand development. Shadow Robot is renowned for pioneering work in advanced dexterous hand development. The company is driven by a passion to create advanced dexterous robots that solve real-world problems and inspire innovation.</p><p dir="ltr">Shadow Robot uses several simulators to test innovations in dexterous hand technology:</p><ul><li dir="ltr"><p dir="ltr">The NVIDIA Isaac robotics simulator, which allows robots to be trained efficiently in a simulated universe. This simulation environment provides data that is diverse enough to effectively train the hand to perform challenging tasks.</p></li><li dir="ltr"><p dir="ltr">The MuJoCo (Multi-Joint dynamics with Contact) physics engine&nbsp;for simulating rigid-body systems and soft-body deformation. This engine provides fast and accurate simulation of how physical bodies interact, with sophisticated replication of kinematics and dynamics.</p></li><li dir="ltr"><p dir="ltr">Gazebo, an open-source simulator with capabilities to test complex scenarios and algorithms in a 3D environment. Gazebo supports a wide range of sensors and provides real-time data for visualization and analysis.</p></li></ul><p dir="ltr">The company’s Dexterous Hand is the most advanced 5-fingered robotic hand in the world. It can manipulate tools and objects with unprecedented accuracy, opening new avenues for research in robotics and artificial intelligence. The Shadow Hand is designed to replicate the human hand as closely as possible and brings the field of robotics closer to overcoming the challenges of dexterous hand development. Shadow Robot’s advancements in dexterous hand technology have the potential to transform industries and daily life.</p><h1 dir="ltr">Conclusion</h1><p dir="ltr">The field of dexterous robotic hands is rapidly evolving. Progress in this field is driven by advancements in technology and research, as well as the potential to reshape how humans live and work. In this article, we have shown that the complexities of robot hand development are numerous, but that promising technologies are emerging to overcome these problems. Researchers have found amazing solutions to solve the challenges of simulation testing, miniaturization, gripping and sensing, as well as the integration of AI and ML.</p><p dir="ltr">Shadow Robot’s vision encompasses the potential for these advancements with their groundbreaking work in dexterous hand technology. The company is positioned to transform industry and daily life. The journey is challenging, but the potential rewards are immense in a future where robotic hands are integrated into our daily life.</p><p dir="ltr">For more information about Shadow Robot, check out their website at <a href="http://www.shadowrobot.com" target="_blank">www.shadowrobot.com</a>.</p><p dir="ltr">You can also read our three-part series on <a href="https://www.wevolver.com/article/dexterous-robotic-hands-part-1-unraveling-the-history-technology-and-applications-of-dexterous-robotic-hands" target="_blank">Dexterous Robotic Hands</a>.</p><h2 dir="ltr">References</h2><p dir="ltr" id="isPasted">[1] “Highly dexterous robot hands can operate in the dark — just like humans”. National Science Foundation (June 9, 2023). Accessed from <a href="https://new.nsf.gov/news/highly-dexterous-robot-hand-can-operate-dark-just" target="_blank">https://new.nsf.gov/news/highly-dexterous-robot-hand-can-operate-dark-just</a>&nbsp;</p><p dir="ltr" id="isPasted">[2] Shuang Li, et al. “A hand-arm teleoperation system for robotic dexterous manipulation”. Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation (2022). Accessed from <a href="https://www.sciencedirect.com/topics/computer-science/robotic-hand" target="_blank">https://www.sciencedirect.com/topics/computer-science/robotic-hand</a>&nbsp;</p><p dir="ltr" id="isPasted">[3] W. Mandil, et. al. “Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation”. Sensors (Basel) (2023). Accessed from <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490130/" target="_blank">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490130/</a>&nbsp;</p><p dir="ltr" id="isPasted">[4] W. Mandil (2023).</p><p dir="ltr" id="isPasted">[5] A. Zewe. “Finger-shaped sensor enables more dexterous robots”. MIT News. (4 Oct. 2023). Accessed from <a href="https://news.mit.edu/2023/finger-shaped-sensor-enables-more-dexterous-robots-1004" target="_blank">https://news.mit.edu/2023/finger-shaped-sensor-enables-more-dexterous-robots-1004</a>&nbsp;</p><p dir="ltr" id="isPasted">[6] U. Kim et al. “Integrated linkage-driven dexterous anthropomorphic robotic hand”. Nature Communications, Vol 12. (14 Dec. 2021). Accessed from <a href="https://www.nature.com/articles/s41467-021-27261-0" target="_blank">https://www.nature.com/articles/s41467-021-27261-0</a>&nbsp;</p><p dir="ltr" id="isPasted">[7] Shadow Robot. Dexterous Hand Series. Accessed from <a href="https://www.shadowrobot.com/dexterous-hand-series/" target="_blank">https://www.shadowrobot.com/dexterous-hand-series/</a>&nbsp;</p><p dir="ltr" id="isPasted">[8] A. Rajeswaran, et. al. “Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations”. Arxiv (2018). Accessed from <a href="https://arxiv.org/abs/1709.10087" target="_blank">https://arxiv.org/abs/1709.10087</a>&nbsp;</p><p dir="ltr" id="isPasted">[9] Shadow Robot. Dexterous Hand Series. Accessed from <a href="https://www.shadowrobot.com/dexterous-hand-series/" target="_blank">https://www.shadowrobot.com/dexterous-hand-series/</a>&nbsp;</p><p dir="ltr" id="isPasted">&nbsp;[10]Shadow Robot. “The benefits of the Shadow Hand for AI and Machine Learning”. Accessed from <a href="https://www.shadowrobot.com/blog/the-benefits-of-the-shadow-hand-for-ai-and-machine-learning/" target="_blank">https://www.shadowrobot.com/blog/the-benefits-of-the-shadow-hand-for-ai-and-machine-learning/</a>&nbsp;</p><p dir="ltr" id="isPasted">[11] A. Rajeswaran, et. al. “Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations”. Arxiv (2018). Accessed from <a href="https://arxiv.org/abs/1709.10087" target="_blank">https://arxiv.org/abs/1709.10087</a>&nbsp;</p><p dir="ltr" id="isPasted">&nbsp;[12]S. Crowe. “10 challenges of using simulators for testing robots”. The Robot Report (May 25, 2020). Accessed from <a href="https://www.therobotreport.com/10-challenges-simulators-robotics-testing/" target="_blank">https://www.therobotreport.com/10-challenges-simulators-robotics-testing/</a>&nbsp;</p><p dir="ltr" id="isPasted">[13] S. Crowe (2020).</p><p dir="ltr" id="isPasted">[14] N. Elangovan, et. al. “An Accessible, Open-Source Dexterity Test: Evaluating the Grasping and Dexterous Manipulation Capabilities of Humans and Robots”. Front Robot AI (2022). Accessed from <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9081435/" target="_blank">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9081435/</a>&nbsp;</p><p dir="ltr" id="isPasted">[15] S. Crowe (2020).</p><p dir="ltr" id="isPasted">[16] S. Crowe (2020).</p><p dir="ltr" id="isPasted">[17] S. Andersson. “Simulation challenges in robotic grasping”. Umeå University (Spring, 2021). Accessed from <a href="https://www.diva-portal.org/smash/get/diva2:1583322/FULLTEXT01.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">https://www.diva-portal.org/smash/get/diva2:1583322/FULLTEXT01.pdf</a>&nbsp;</p><p dir="ltr" id="isPasted">[18] Shadow Robot. “Simulating the Shadow Hand”. (1 Aug. 2023). Accessed from <a href="https://www.shadowrobot.com/news/press-releases/simulating-the-shadow-hand/" target="_blank">https://www.shadowrobot.com/news/press-releases/simulating-the-shadow-hand/</a>&nbsp;</p>

Simulation is key to the efficient development of dexterous robot hands with human-like abilities. 


Navigating Technological Challenges for Dexterous Hands

<h1 dir="ltr" id="isPasted">Introduction</h1><p dir="ltr">Virtual prototyping refers to the use of computer simulations to create and test digital models of physical systems before they are built. As semiconductor and OEM companies grapple with the integration of intricate hardware and software for the next wave of wireless, consumer, and automotive devices, the old guard of serialized development staggers under the weight of new demands. Virtual prototyping slices through these challenges, offering a versatile platform for early software development and meticulous system validation, reshaping timelines, and enhancing product quality.</p><p dir="ltr">This article discusses virtual prototyping, contrasting it with traditional verification methodologies to reveal its role in accelerating time to market and bolstering communication across the supply chain. Readers will navigate through the transformative powers of virtual prototyping, from the inception of software development pre-silicon availability to the intricacies of system validation. The article uncovers the facets of virtual prototyping tools as we delve into how these innovations forge a path for seamless design verification. &nbsp;We use Synopsys' tools and services to illustrate how virtual prototyping is becoming an indispensable asset in digital design.</p><h1 dir="ltr">Understanding Virtual Prototyping</h1><p dir="ltr">Virtual prototyping is defined as the process of using computer simulations to model and validate complex system designs before manufacturing their physical components. This method is increasingly important due to the growing complexity and enhanced functionality of modern systems, which often exceed the capabilities of traditional prototyping methods. By employing virtual prototyping, designers can conduct detailed and comprehensive testing of systems, ensuring they meet stringent performance criteria. This approach not only enables early detection and resolution of potential design issues but also facilitates more efficient development cycles, aligning with the advanced requirements of current digital design practices.</p><p dir="ltr">The intrinsic value of virtual prototyping lies in its ability to front-load the design process. It provides an interactive, software-based environment where designs can be tested, analyzed, and optimized. Engineers can simulate the behaviors of embedded systems under a wide array of operational conditions and use cases, enabling them to iterate more rapidly and with greater confidence. This simulation-centric approach empowers teams to validate assumptions, evaluate system performance, and conduct feasibility studies without the costs and time constraints associated with physical prototypes.</p><h2 dir="ltr">Accelerating Product Development with Virtual Prototyping</h2><p dir="ltr">A notable feature of virtual prototyping is its contribution to embedded software development. It allows software engineers to begin their design and testing work in parallel with, or even in advance of, hardware development. This overlap in development cycles is not feasible with conventional prototyping, which requires a sequential process where software development is contingent on the availability of the hardware.</p><p dir="ltr">Additionally, virtual prototyping aids in optimizing the implementation of systems. Design teams can explore different architectural choices and hardware configurations to arrive at the most efficient and cost-effective solutions. This systematic exploration is essential for managing power consumption, processing speed, and overall system reliability – factors that are critical in today’s competitive technology landscape.</p><p dir="ltr">Moreover, virtual prototypes can be integrated into automated testing environments, enabling continuous integration and delivery pipelines for software development. This integration leads to high-quality software being developed and maintained, with the capability to quickly adapt to changing requirements or identify potential issues early in the design cycle.</p><p dir="ltr">In the broader sense, virtual prototyping transcends beyond the confines of mere pre-silicon validation. It stands as a strategic enabler for lifecycle management, facilitating updates, feature enhancements, and maintenance long after the product has been deployed. As systems become increasingly interconnected and expected to support new functionalities over time, virtual prototyping offers the flexibility and scalability needed to sustain product evolution.</p><p dir="ltr">Thus, the role of virtual prototyping is multifaceted: it accelerates the design process, enhances product quality, reduces development costs, and extends the lifecycle of digital products. It encapsulates the shift towards a more agile, forward-thinking approach in digital design, one that is instrumental for companies looking to thrive amidst the burgeoning complexities of modern technological innovation.</p><h1 dir="ltr">Virtual Prototyping Versus Traditional Methods</h1><p dir="ltr">In digital design and verification, the cost of error escalates as one moves through the development process. Traditional verification methodologies are increasingly seen as inadequate, especially as the complexity of integrated systems grows. This section will conduct a comparative analysis of virtual prototyping against these traditional methodologies, illustrating the efficiencies gained through modern techniques.</p><h2 dir="ltr">The Stumbling Blocks of Traditional Verification</h2><p dir="ltr">In traditional verification, the development of software often lags behind hardware completion. This approach is inherently sequential and leaves little room for iterative improvement, which can be particularly detrimental to aggressive product development schedules. Software teams await the physical prototypes to test their code, creating a bottleneck that impedes early detection and resolution of issues.</p><p dir="ltr">Furthermore, traditional methods often rely on physical testing environments, which can be both costly and limited in scope. The limitations of hardware-based tests—such as the number of available prototypes, the difficulty in re-creating certain operating conditions, and the inability to test every possible scenario—can leave critical bugs undetected until late in the development process.</p><h2 dir="ltr">The Strategic Edge of Virtual Prototyping</h2><p dir="ltr">Virtual prototyping offers a stark contrast, delivering a strategic edge by removing the reliance on hardware availability. Software development can commence much earlier, even before the RTL (Register-Transfer Level) stages of hardware design. This shift not only circumvents the wait for physical prototypes but also opens up parallel workflows that can dramatically accelerate the development cycle.</p><h2 dir="ltr">Efficiency and Scope of Testing</h2><p dir="ltr">Virtual prototypes enable an extensive scope of testing that physical prototypes cannot match. By simulating various system states and operational conditions, virtual prototypes allow for an exhaustive verification process, including the kind of edge cases that are difficult to replicate with hardware. The flexibility of virtual environments means that software testing and system validation can occur at multiple workstations concurrently, greatly expanding test coverage.</p><h2 dir="ltr">Cost-Effectiveness and Error Detection</h2><p dir="ltr">From a financial perspective, virtual prototyping is compelling. By detecting errors early in the design process, it mitigates the high costs associated with late-stage troubleshooting in traditional verification. Early detection not only reduces the expenditure on rectifications but also minimizes the risk of expensive recalls and brand damage post-product release.</p><h2 dir="ltr">Debugging and Analysis</h2><p dir="ltr">The debug capabilities of virtual prototyping are a leap forward in verification methodology. The ability to pause, rewind, and replay scenarios in a virtual prototype provides an unprecedented level of control and insight into system behavior. This allows for a more nuanced analysis and a thorough understanding of the root causes of issues, which can be elusive in traditional post-silicon debugging.</p><h2 dir="ltr">The Quantifiable Benefits</h2><p dir="ltr">The advantages of virtual prototyping can be quantified in reduced development time, increased product quality, and lowered costs. With the accelerated time-to-market that virtual prototyping allows, companies can stay ahead of the competition and better meet market demands. Additionally, the increased productivity and efficiency across teams contribute to a more streamlined development process, from conceptual design to final product validation.</p><h1 dir="ltr">Virtual Prototyping Solutions by Synopsys</h1><p dir="ltr">Synopsys offers a comprehensive virtual prototyping solution that includes several key products and services:</p><ul><li dir="ltr"><p dir="ltr"><strong>Synopsys Virtualizer:</strong> The <a href="https://www.synopsys.com/verification/virtual-prototyping/virtualizer.html" target="_blank">Synopsys Virtualizer tool</a> enables the creation of virtual prototypes that can be used for early software development and testing. This tool is a part of a suite that includes Synopsys Platform Architect™ and a vast array of transaction-level models (TLMs). <a href="https://www.synopsys.com/verification/virtual-prototyping/vdk.html" target="_blank">Virtualizer Development Kits (VDKs)</a> are also provided for software development, allowing for early software bring-up before silicon availability.</p></li><li><strong>Hybrid Prototyping:</strong> For hardware and software engineers, Synopsys offers a <a href="https://www.synopsys.com/verification/virtual-prototyping/virtualizer/hybrid-prototyping.html" target="_blank">hybrid prototyping solution</a> that combines virtual and FPGA-based prototypes. This allows for the benefits of both approaches, enabling the start of multi-core SoC prototyping earlier and facilitating high-performance execution of system-level models, which include direct connections to real-world hardware interfaces.</li></ul><p><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAwMjAxNDU1OTAyLWltYWdlMi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="hybrid-prototyping-framework-synopsis"><span class="fr-inner" spellcheck="false">A block diagram illustrating the hybrid prototyping framework and workflow. Source:&nbsp;<a href="https://www.youtube.com/watch?v=_ux9xPkGfF0&ab_channel=Synopsys" target="_blank" rel="noopener noreferrer">Faster Software Development using Hybrid Prototyping - Synopsys YouTube</a></span></span></span></p><ul><li dir="ltr"><strong>Synopsys Platform Architect for Multi-Core Optimization (MCO):</strong> Synopsys provides the&nbsp;<a href="https://www.synopsys.com/verification/virtual-prototyping/platform-architect.html" target="_blank">Platform Architect tool</a> for creating virtual prototypes geared towards architecture design. This tool is designed to cater to the specific requirements for simulation speed and timing accuracy, and it is used alongside Virtualizer for a comprehensive virtual prototyping environment tailored to software development use cases.</li><li dir="ltr"><strong>Virtual Prototyping Services:</strong> Synopsys provides full-service solutions where they take charge of modeling, assembling, and delivering a virtual prototype tailored to the specific needs of end-user teams. This service may include integrating software tools, environment models, test environments, and customizing virtual prototype tools for debugging and analysis to meet particular requirements.</li></ul><p dir="ltr">Each of these offerings is designed to support different aspects of the development process, from the early stages of software development to the optimization of multi-core system architectures.</p><h1 dir="ltr">Conclusion</h1><p dir="ltr">Virtual prototyping marks a turning point in digital design, fundamentally transforming the way we approach the development of complex systems. By allowing for early software development and thorough system validation, it significantly shortens time-to-market and enhances product quality. The ability to test and iterate designs within a virtual environment translates to a leap in efficiency and cost-effectiveness, driving innovation at a faster clip than ever before.</p><p dir="ltr">As the complexities of design increase, virtual prototyping offers the agility and foresight needed to keep pace with rapid technological advancements. It is more than a tool; it is a strategic imperative for any organization aiming to lead in the fiercely competitive landscape of digital technology.</p><h1 dir="ltr">References</h1><p dir="ltr">[1] What is Virtual Prototyping? – How it Works &amp; Benefits. Synopsys; [Online]. Available from:&nbsp;<a href="https://www.synopsys.com/glossary/what-is-virtual-prototyping.html" target="_blank">https://www.synopsys.com/glossary/what-is-virtual-prototyping.html</a></p><p dir="ltr">[2] Chang KH. Introduction to e-Design. In: Chang KH, editor. Design Theory and Methods Using CAD/CAE. Academic Press; 2015. p. 1-37. [Online]. Available from:&nbsp;<a href="https://www.sciencedirect.com/science/article/abs/pii/B9780123985125000013" target="_blank">https://www.sciencedirect.com/science/article/abs/pii/B9780123985125000013</a></p><p dir="ltr">[3] Bidanda B, Bártolo PJ, editors. Virtual Prototyping &amp; Bio Manufacturing in Medical Applications. Springer International Publishing; 2020.</p>

Virtual prototyping in digital design streamlines the development of complex systems by allowing early software testing and hardware optimization before the creation of a physical prototype.

The Era of Virtual Prototyping in Digital Design

<p id="isPasted"><strong>New mobility offers such as ridepooling could relieve pressure on city centers, reduce emissions and offer passengers more flexibility.&nbsp;The “Autonomous Ridepooling” (ALIKE) project, in which researchers from the Karlsruhe Institute of Technology (KIT) are also involved, is developing an on-demand transport service with autonomous shuttle buses in Hamburg’s road traffic.&nbsp;ALIKE bundles the competencies of mobility providers and partners from the vehicle industry and science.&nbsp;The Federal Ministry of Digital and Transport is funding the project with 26 million euros.</strong></p><p>Up to 10,000 autonomous shuttles could be on Hamburg's streets by 2030.&nbsp;With a modern on-demand transport offer, a mobility solution is to be created in the Hanseatic city that expands the classic public transport of buses and trains with a new product.&nbsp;The system should be easy for passengers to book and use, ideally scalable across regions and also transferable to rural areas.</p><p>Autonomous ridepooling is intended to close the gap between actual mobility needs in many life situations and public transport.&nbsp;For the first time, the consortium partners will develop and build a complete system for booking and using up to 20 autonomous vehicles in public transport in Hamburg.&nbsp;The vehicles will be integrated into an on-demand service in order to test them in real operation and make them digitally bookable for users via apps.&nbsp;The project results are intended to create the basis for future commercial deployment and scaling of autonomous ridepooling services.</p><h2><strong>Impact of autonomous vehicles on traffic and passengers</strong></h2><p>Researchers from the KIT Institute for Transport (IfV) are investigating how the Hamburg population accepts the new offer.&nbsp;In doing so, they build on the&nbsp;<a href="https://www.kit.edu/kit/pi_2021_110_verkehrssimulation-ridepooling-fordert-verkehrswende.php" target="_blank">accompanying research in the MOIA project</a> , in which they were able to show that car traffic in Hamburg could be reduced by up to eight percent under certain conditions with ridepooling offers.&nbsp;The specially developed simulation tool mobiTopp will also be used at ALIKE.&nbsp;“This is where the first autonomous vehicles are being integrated into public transport on a large scale.&nbsp;We are investigating how people behave with autonomous ridepooling offers,” explains Dr.&nbsp;Martin Kagerbauer from IfV.&nbsp;Ridepooling can be operated more economically with autonomous vehicles.&nbsp;This also strengthens mobility away from private cars, according to the mobility researcher.&nbsp;Using the data from these surveys, he and his team then further develop the MOIA traffic model with mobiTopp.&nbsp;“The more data we collect on different forms of mobility in our model, the better we can understand and advance the transport transition.”</p><p>The Federal Ministry of Digital and Transport is supporting the project with 26 million euros.&nbsp;Federal Transport Minister Volker Wissing handed over the funding decision today (October 23, 2023) in Hamburg.</p><h2><strong>Partner in ALIKE</strong></h2><p>In addition to HOCHBAHN as consortium leader, the project consortium includes the on-demand service MOIA, the vehicle manufacturers HOLON and Volkswagen Commercial Vehicles, KIT as a research partner and the Hamburg Authority for Transport and Mobility Transition.</p>

Simulation of ridepooling vehicles with mobiTopp for researching the effects of autonomous traffic (Fig.: MOIA Simulation)

Federal government funds project in the model region of Hamburg - KIT researchers are investigating the behavior of users of mobility offers

Autonomous Ridepooling to Support the Traffic Turnaround

<p id="isPasted">In wind-tunnel tests, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt; DLR) has investigated the lift generated by commercial aircraft using state-of-the-art measurement and simulation technologies. The findings will help to predict the characteristics of future aircraft much more accurately than before, in order to make them quieter, more efficient and more environmentally friendly.</p><p>The development of a new aircraft is a lengthy and complex process. An interaction between computer simulations and wind tunnel experiments takes place long before construction and the first flight. "Highly accurate simulations of the airflow around an aircraft are necessary to accurately and reliably predict the aerodynamics and thus the flight envelope of current and future aircraft configurations," says Project Coordinator Cornelia Grabe of the&nbsp;<a href="https://www.dlr.de/en/as" target="_blank">DLR Institute of Aerodynamics and Flow Technology</a> in&nbsp;<a href="https://www.dlr.de/en/dlr/locations-and-offices/goettingen" target="_blank">Göttingen</a>. The same applies to identifying the safe operating range of the engines. The&nbsp;<a href="https://www.dlr.de/as/en/desktopdefault.aspx/tabid-17581/27883_read-72278/" target="_blank" rel="noopener noreferrer">DLR 'Adaptive Data-driven Physical Modelling towards Border of Envelope Applications' (ADaMant)</a> project aims to develop and demonstrate computer models for such high-precision flow simulations.</p><h2>Joint endeavour by DLR and NASA</h2><p>DLR and NASA worked together to carry out tests at the Braunschweig&nbsp;<a href="https://www.dnw.aero/wind-tunnels/nwb/" target="_blank" rel="noopener noreferrer">Low-Speed Wind Tunnel (Niedergeschwindigkeits-Windkanal Braunschweig; NWB)</a>, operated by&nbsp;<a href="https://www.dnw.aero/" target="_blank" rel="noopener noreferrer">German-Dutch Wind Tunnels (DNW)</a>, using an internationally recognised research model of a commercial aircraft provided by the US National Aeronautics and Space Administration (<a href="https://www.nasa.gov/" target="_blank" rel="noopener noreferrer">NASA)</a>. Tests with the same model are being repeated in NASA's own wind tunnel, in order to acquire a better understanding of how the use of different wind tunnels affects the resulting measurement data. The airflow around the research model in free flight was examined in the flow simulation, and the wind tunnel was also modelled. The aim was to investigate the extent to which the wind tunnel influences the flow around an aircraft model. This knowledge will make computer simulations and wind tunnel experiments more comparable.</p><h2>The challenge of turbulent flows</h2><p>The greatest challenge in the computer modelling of aerodynamic processes is turbulent flows. They require sophisticated computational methods to find a balance between precision and efficiency. While precision is essential to reliably predict the flight envelope of an aircraft or the operating range of an engine, the use of resources such as computing time on supercomputers needs to be reduced to the minimum necessary for such simulations.</p><p>In the ADaMant project, researchers examined computer models with varying degrees of precision and evaluated them with regard to their suitability both for simulating the airflow around an aircraft's exterior under specific flight conditions and for simulating certain operating ranges of an engine. Ultimately, the knowledge gained should help to improve the computer models used to design and simulate the flight characteristics of future aircraft and engines. This will support and accelerate the development of more efficient and environmentally friendly aircraft.</p><p>The wind tunnel tests took place inside DNW's Braunschweig Low-Speed Wind Tunnel. The calculations were carried out using DLR's&nbsp;<a href="https://www.dlr.de/en/research-and-transfer/research-infrastructure/hpc-cluster/caro" target="_blank">CARO supercomputer</a> in Göttingen. Six DLR institutes and facilities are involved in the project:</p>

The NASA aircraft model inside the DNW Braunschweig Low-Speed Wind Tunnel (DNW-NWB). 'Sheets' of laser light illuminating the inner and outer wings are used to measure flow velocities. Credit: DLR (CC BY-NC-ND 3.0)

In wind-tunnel tests, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt; DLR) has investigated the lift generated by commercial aircraft using state-of-the-art measurement and simulation technologies.

DLR improves the prediction of flight characteristics for future aircraft

<section data-loopindex="1" data-theme="light" id="isPasted"><div data-loopindex="1"><p dir="ltr">Slicing software has been around since the very beginning of 3D printing, with digital slicing of STL files being one of the key features of Chuck Hull&rsquo;s first stereolithography system. However, in the time since, slicing software has evolved beyond its base functions of simply converting 3D object models into machine instructions.</p><p dir="ltr">Today, enterprise-scale software platforms like Eiger not only provide slicing&mdash; they serve as an organization&rsquo;s digital hub for&nbsp;<a href="https://markforged.com/resources/blog/additive-manufacturing-101-guide-the-basics" target="_blank">additive manufacturing</a> activities such as managing printer fleets, digital inventories of parts, and accessing usage analytics.</p><p dir="ltr">Also, new&nbsp;<a href="https://markforged.com/software" target="_blank">3D printing software</a> tools now exist to further streamline and automate additive manufacturing operations, which can save organizations large amounts of time and money while making life easier.</p><p dir="ltr">Curious to learn about today&rsquo;s available 3D printing software? Read this article for an introduction.</p></div></section><section data-loopindex="2" data-theme="light"><div data-loopindex="2"><h3></h3><h2 dir="ltr"><strong>Slicing software: more than just print settings</strong></h2><p dir="ltr">A slicing program transforms a digital model into a set of instructions that the 3D printer can use to produce a tangible part. It takes a&nbsp;<a href="https://markforged.com/resources/blog/how-to-create-high-quality-stl-files-for-3d-prints" target="_blank">3D model</a>, &lsquo;slices&rsquo; it into the many distinct horizontal layers that together comprise a 3D-printed part, while customizing print settings and generating toolpaths &mdash; converting all of this information into machine-readable instructions for the printer to follow. Slicers allow users to define key parameters for each print &mdash; such as print orientations, layer heights, support structures, print speeds, infill densities, continuous fiber reinforcement, and other settings.</p><p dir="ltr">Certain modern slicing software programs, such as Eiger, also serve as an organization&rsquo;s digital hub for managing nearly all 3D printing activities. Along with base slicing functions, cloud-based applications like&nbsp;<a href="https://markforged.com/management-integration" target="_blank">Eiger</a> have features for managing part files, multiple printers, simultaneous print jobs, and users within an organization (through role-based access control), as well as tracking performance and usage analytics.</p><p dir="ltr">With many global manufacturers using 3D printing for core operations, Markforged designed Eiger software to be a simple means for managing print jobs across printers in different geographic locations &mdash; making this also a powerful supply chain tool for fast, on-demand procurement of parts. Storing parts in the cloud as digital inventories also allows manufacturers to avoid allocating valuable space for physical inventories of spare parts.</p><p dir="ltr"><span class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable" contenteditable="false" draggable="true"><iframe width="640" height="360" src="https://www.youtube.com/embed/FfN9-Z9egCw?&wmode=opaque&rel=0&enablejsapi=1&origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><em>How can we extend the benefits of 3D printed parts to worksites across the globe? Being able to 3D print parts at the point of need makes operations more agile, efficient, and cost-effective.&nbsp;</em></p><section data-loopindex="4" data-theme="light" id="isPasted"><div data-loopindex="4"><h3></h3><h2 dir="ltr"><strong>API integrations: automate workflows between 3D printing, factory systems</strong></h2><p dir="ltr">Through API integrations, different factory systems (like ERP or MES systems) can speak to each other, syncing data. Integration offers&nbsp;<a href="https://markforged.com/resources/blog/why-integrate-factory-systems-with-3d-printing-software#:~:text=Benefits%20of%203D%20printing%20software%20integration" target="_blank">a number of benefits</a>. Successful integrations, through automating processes and information transfer, ultimately save time, reduce errors, and reduce delays that take place between actions.</p><p dir="ltr">Through an API integration, slicing software like Eiger can be integrated with other factory systems, automating additive-related workflows at other digital manufacturing touchpoints. Other important business systems can automatically receive details from the slicer around their parts, builds, print jobs, and printers. For example, a slicer can automatically prompt an asset management system to track each printed part &mdash; or automatically prompt a Digital Kanban Inventory system to initiate a print when inventory falls beneath a defined threshold.</p><p dir="ltr">Through API integrations, slicers can connect with many different factory systems: a Manufacturing Execution System (MES), Product Lifecycle Management (PLM) system, or Enterprise Resource Planning (ERP) system, QMS, CRM, AM/EAM, SCM, financials, accounting systems, and more.</p></div></section><section data-loopindex="5" data-theme="light"><div data-loopindex="5"><h3></h3><h2 dir="ltr"><strong>Simulating part performance: determining strength and stiffness before hitting &lsquo;print&rsquo;</strong></h2><p dir="ltr">For many 3D printed parts &mdash; from tooling built for factory floors, to&nbsp;<a href="https://markforged.com/resources/blog/3-ways-to-deliver-trusted-parts-faster-with-simulation" target="_blank">composite steering wheels</a> guiding drag racing vehicles that reach speeds of 275+ mph &mdash; high strength and stiffness are absolutely crucial. Furthermore, as many organizations operate on development and production cycles with limited time, it&rsquo;s valuable to know that your part meets its performance requirements without having to print and break the design many times.</p><p dir="ltr"><a href="https://markforged.com/simulation" target="_blank">3D printing simulation software</a> allows organizations to virtually test part strength and stiffness, where users can validate how their part will perform&nbsp;<strong>before&nbsp;</strong>they print it. Simulation software dramatically reduces the need for print-break test cycles, which typically consume significant amounts of time and material. 3D printing simulation software can also optimize prints to reduce material costs and/or print times, while still meeting defined performance requirements.</p><p dir="ltr">Simulation software works by using information about strength and stiffness requirements, the use case, and the part itself. After the user specifies and inputs the necessary testing criteria and performance factors, the software runs its simulation to determine if the part passes or fails the virtual performance test.</p><p dir="ltr"><span class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable" contenteditable="false" draggable="true"><iframe width="640" height="360" src="https://www.youtube.com/embed/20eHQzZ-FGQ?&wmode=opaque&rel=0&enablejsapi=1&origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><em>Learn how simulating part performance can help cut print times, increase part stiffness, all while maintaining strength and cost.&nbsp;</em></p><section data-loopindex="7" data-theme="light" id="isPasted"><div data-loopindex="7"><h3></h3><h2 dir="ltr"><strong>In-process quality inspection</strong></h2><p dir="ltr">Given the requisite hardware and software, certain 3D printers can scan parts for quality assurance inspection during the printing process. Once printing is finished, the inspection software automatically generates a report that shows you if your part is within spec via clear pass/fail analysis.</p><p dir="ltr"><a href="https://markforged.com/inspection" target="_blank">3D printing inspection software</a> works by using an onboard laser micrometer and a scanning process. It generates a laser inspection toolpath at slicing time for your part and build settings. When the part begins printing, the software automates the collection, upload, and registration of measurement data to help users ascertain if each part printed is within the acceptable tolerance.</p><p dir="ltr">Ultimately, automated inspection of parts boosts manufacturing productivity by reducing manpower and time required for manual quality assurance.</p></div></section><section data-loopindex="8" data-theme="light"><div data-loopindex="8"><h3></h3><h2><strong>Is 3D printing software secure?</strong></h2><p dir="ltr">A single cybersecurity breach can be incredibly costly for any business hit. With organizations now storing sensitive data in the cloud, information security has topical importance in&nbsp;<a href="https://markforged.com/3d-printers" target="_blank">industrial 3D printing</a> &mdash; after all, when storing proprietary parts in the cloud as digital inventory, manufacturers need to protect their intellectual property.</p><p dir="ltr">The Digital Forge, including Eiger software, is the first additive manufacturing platform to attain<strong>&nbsp;ISO/IEC 27001</strong> certification, meeting rigorous international standards for data management and information security.</p><p dir="ltr"><span class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable" contenteditable="false" draggable="true"><iframe width="640" height="360" src="https://www.youtube.com/embed/r_wczXR9sSI?&wmode=opaque&rel=0&enablejsapi=1&origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><em>How does Markforged secure your data? </em></p><section data-loopindex="10" data-theme="light" id="isPasted"><div data-loopindex="10"><p dir="ltr">Markforged 3D printing software uses&nbsp;<strong>SAML SSO</strong> (single sign-on) and&nbsp;<strong>TLS</strong> (transport layer security) certificates. SAML SSO provides a simplified authentication process across enterprise applications with the benefit of improved security. As only one set of credentials is needed per user, user credentials for the organization do not need to be stored in any system. TLS certificates ensure critical information passed from web applications is securely encrypted &mdash; to prevent unwanted modifications, loss, or theft of important data.</p><p><strong>RBAC&nbsp;</strong>(role-based access control) in Markforged 3D printing software offers additional benefits for data management and information security. Ensuring employees only have access to the information they need improves confidentiality, privacy, and compliance. RBAC protects sensitive data from unwanted access, modification, leaks, or deletion &mdash; while also simplifying organizational user management processes.</p></div></section><section data-loopindex="11" data-theme="light"><div data-loopindex="11"><h3></h3><h2 dir="ltr"><strong>3D printing software has evolved substantially. What&rsquo;s next?</strong></h2><p dir="ltr">3D printing software has come a long way since back when it was introduced to serve a single purpose.</p><p dir="ltr">Today, innovative slicing applications like Eiger have expanded how 3D printing software can create value for manufacturers beyond just the domain of slicing, evolving into a new industry benchmark: enterprise-grade control centers for additive manufacturing operations. This modern 3D printing software has created new ways for 3D printers to manage inventory, solve supply chain problems, and easily manage all 3D printing operations.</p><p dir="ltr">As more manufacturers digitally transform to make operations leaner and more efficient, the role of industrial 3D printing continues to grow. Today&rsquo;s cutting edge software tools &mdash; such as performance simulation, slicing APIs, and in-process inspection &mdash; continue to make industrial 3D printing an even faster, more efficient, and more automated solution for solving the biggest challenges in manufacturing.</p></div></section></div></section></div></section></div></section>

Slicing software has been around since the very beginning of 3D printing, with digital slicing of STL files being one of the key features of Chuck Hull’s first stereolithography system. However, in the time since, slicing software has evolved beyond its base functions of simply converting 3D object models into machine instructions.

From Slicers to Performance Simulators: Evolution of Industrial 3D Printing Software

<p>This article was first published on&nbsp;<a href="https://news.mit.edu/2023/helping-robots-handle-fluids-0524" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>. &nbsp;</p><hr><p id="isPasted">Imagine you&rsquo;re enjoying a picnic by a riverbank on a windy day. A gust of wind accidentally catches your paper napkin and lands on the water&rsquo;s surface, quickly drifting away from you. You grab a nearby stick and carefully agitate the water to retrieve it, creating a series of small waves. These waves eventually push the napkin back toward the shore, so you grab it. In this scenario, the water acts as a medium for transmitting forces, enabling you to manipulate the position of the napkin without direct contact.</p><p>Humans regularly engage with various types of fluids in their daily lives, but doing so has been a formidable and elusive goal for current robotic systems. Hand you a latte? A robot can do that. Make it? That&rsquo;s going to require a bit more nuance.&nbsp;</p><p><a href="https://openreview.net/forum?id=Cp-io_BoFaE" target="_blank">FluidLab</a>, a new simulation tool from researchers at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), enhances robot learning for complex fluid manipulation tasks like making latte art, ice cream, and even manipulating air. The virtual environment offers a versatile collection of intricate fluid handling challenges, involving both solids and liquids, and multiple fluids simultaneously. FluidLab supports modeling solid, liquid, and gas, including elastic, plastic, rigid objects, Newtonian and non-Newtonian liquids, and smoke and air.&nbsp;</p><p>At the heart of FluidLab lies FluidEngine, an easy-to-use physics simulator capable of seamlessly calculating and simulating various materials and their interactions, all while harnessing the power of graphics processing units (GPUs) for faster processing. The engine is &ldquo;differential,&rdquo; meaning the simulator can incorporate physics knowledge for a more realistic physical world model, leading to more efficient learning and planning for robotic tasks. In contrast, most existing reinforcement learning methods lack that world model that just depends on trial and error. This enhanced capability, say the researchers, lets users experiment with robot learning algorithms and toy with the boundaries of current robotic manipulation abilities.</p><p>To set the stage, the researchers tested said robot learning algorithms using FluidLab, discovering and overcoming unique challenges in fluid systems. By developing clever optimization methods, they&#39;ve been able to transfer these learnings from simulations to real-world scenarios effectively.&nbsp;</p><p>&ldquo;Imagine a future where a household robot effortlessly assists you with daily tasks, like making coffee, preparing breakfast, or cooking dinner. These tasks involve numerous fluid manipulation challenges. Our benchmark is a first step towards enabling robots to master these skills, benefiting households and workplaces alike,&rdquo; says visiting researcher at MIT CSAIL and research scientist at the MIT-IBM Watson AI Lab Chuang Gan, the senior author on a new paper about the research. &ldquo;For instance, these robots could reduce wait times and enhance customer experiences in busy coffee shops. FluidEngine is, to our knowledge, the first-of-its-kind physics engine that supports a wide range of materials and couplings while being fully differentiable. With our standardized fluid manipulation tasks, researchers can evaluate robot learning algorithms and push the boundaries of today&rsquo;s robotic manipulation capabilities.&rdquo;</p><h2><strong>Fluid fantasia&nbsp;</strong></h2><p>Over the past few decades, scientists in the robotic manipulation domain have mainly focused on manipulating rigid objects, or on very simplistic fluid manipulation tasks like pouring water. Studying these manipulation tasks involving fluids in the real world can also be an unsafe and costly endeavor.&nbsp;</p><p>With fluid manipulation, it&rsquo;s not always just about fluids, though. In many tasks, such as creating the perfect ice cream swirl, mixing solids into liquids, or paddling through the water to move objects, it&rsquo;s a dance of interactions between fluids and various other materials. Simulation environments must support &ldquo;coupling,&rdquo; or how two different material properties interact. Fluid manipulation tasks usually require pretty fine-grained precision, with delicate interactions and handling of materials, setting them apart from straightforward tasks like pushing a block or opening a bottle.&nbsp;</p><p>FluidLab&rsquo;s simulator can quickly calculate how different materials interact with each other.&nbsp;</p><p>Helping out the GPUs is &ldquo;Taichi,&rdquo; a domain-specific language embedded in Python. The system can compute gradients (rates of change in environment configurations with respect to the robot&#39;s actions) for different material types and their interactions (couplings) with one another. This precise information can be used to fine-tune the robot&rsquo;s movements for better performance. As a result, the simulator allows for faster and more efficient solutions, setting it apart from its counterparts.</p><p>The 10 tasks the team put forth fell into two categories: using fluids to manipulate hard-to-reach objects, and directly manipulating fluids for specific goals. Examples included separating liquids, guiding floating objects, transporting items with water jets, mixing liquids, creating latte art, shaping ice cream, and controlling air circulation.&nbsp;</p><p>&ldquo;The simulator works similarly to how humans use their mental models to predict the consequences of their actions and make informed decisions when manipulating fluids. This is a significant advantage of our simulator compared to others,&rdquo; says Carnegie Mellon University PhD student Zhou Xian, another author on the paper. &ldquo;While other simulators primarily support reinforcement learning, ours supports reinforcement learning and allows for more efficient optimization techniques. Utilizing the gradients provided by the simulator supports highly efficient policy search, making it a more versatile and effective tool.&rdquo;</p><h2><strong>Next steps</strong></h2><p>FluidLab&rsquo;s future looks bright. The current work attempted to transfer trajectories optimized in simulation to real-world tasks directly in an open-loop manner. For next steps, the team is working to develop a closed-loop policy in simulation that takes as input the state or the visual observations of the environments and performs fluid manipulation tasks in real time, and then transfers the learned policies in real-world scenes.</p><p>The platform is publicly&nbsp;<a href="https://fluidlab2023.github.io/" target="_blank"><u>publicly available</u></a>, and researchers hope it will benefit future studies in developing better methods for solving complex fluid manipulation tasks.<br><br>&ldquo;Humans interact with fluids in everyday tasks, including pouring and mixing liquids (coffee, yogurts, soups, batter), washing and cleaning with water, and more,&rdquo; says University of Maryland computer science professor Ming Lin, who was not involved in the work. &ldquo;For robots to assist humans and serve in similar capacities for day-to-day tasks, novel techniques for interacting and handling various liquids of different properties (e.g. viscosity and density of materials) would be needed and remains a major computational challenge for real-time autonomous systems. This work introduces the first comprehensive physics engine, FluidLab, to enable modeling of diverse, complex fluids and their coupling with other objects and dynamical systems in the environment. The mathematical formulation of &lsquo;differentiable fluids&rsquo; as presented in the paper makes it possible for integrating versatile fluid simulation as a network layer in learning-based algorithms and neural network architectures for intelligent systems to operate in real-world applications.&rdquo;</p><hr><p>Gan and Xian wrote the paper alongside Hsiao-Yu Tung a postdoc in the MIT Department of Brain and Cognitive Sciences; Antonio Torralba, an MIT professor of electrical engineering and computer science and CSAIL principal investigator; Dartmouth College Assistant Professor Bo Zhu, Columbia University PhD student Zhenjia Xu, and CMU Assistant Professor Katerina Fragkiadaki. The team&rsquo;s research is supported by the MIT-IBM Watson AI Lab, Sony AI, a DARPA Young Investigator Award, an NSF CAREER award, an AFOSR Young Investigator Award, DARPA Machine Common Sense, and the National Science Foundation.</p><hr><p>&quot;Reprinted with permission of&nbsp;<a href="https://news.mit.edu/2023/helping-robots-handle-fluids-0524" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>&quot;  &nbsp;</p>

Researchers created "FluidLab," a simulation environment with a diverse set of manipulation tasks involving complex fluid dynamics. Image: Alex Shipps/MIT CSAIL via Midjourney

Researchers create a new simulation tool for robots to manipulate complex fluids in a step toward helping them more effortlessly assist with daily tasks.

Helping robots handle fluids

<h2 id="isPasted">Some Historical Perspective</h2><p>The relationship between physical engineering and the programmable computer is as old as the computer itself. The reality is that, for most of us, crunching lots of numbers by hand is frustrating, time-consuming, and error-prone.</p><p>The British government of the 1820s agreed, opting to fund the early development of Charles Babbage&#39;s difference engines. These were visually striking machines: large mechanical computers, some capable of solving 7th order polynomials, that were intended to dramatically reduce the cost of production of tabulated trigonometric and logarithmic functions &mdash; the kind that were incredibly useful to scientists, engineers and navigators in the immediate aftermath of the Industrial Revolution.</p><p>Fast forward to 1945, and we see the first glimpse of modernity: ENIAC, the world&#39;s first programmable, electronic, general-purpose digital computer. If you&#39;re curious as to what was it designed for, the answer is the computation of artillery and mortar firing tables; a task that involves exactly the kind of Newtonian mechanics calculations that will be familiar to any undergraduate mechanical or aerospace engineering student (see: Mayevski&#39;s <a href="https://books.google.co.uk/books/about/Trait%C3%A9_de_balistique_ext%C3%A9rieure.html?id=jE_zAAAAMAAJ&redir_esc=y" target="_blank"><em>Trait&eacute; de balistique ext&eacute;rierure</em>&nbsp;</a>, or the US Army Ballistic Research Laboratories&#39; <a href="https://asset-pdf.scinapse.io/prod/77299953/77299953.pdf" target="_blank"><em>The Production of Firing Tables for Cannon Artillery</em></a>).</p><p>Just a decade or so after ENIAC, IBM drew up its first specifications for a new programming language, <em>The IBM Mathematical Formula Translating System</em>, familiar to many of us as FORTRAN. Developed expressly for science and engineering, FORTRAN is a true titan of the scientific computing world. If you&#39;ve ever fired up MATLAB, analysed a load-case with an <a href="https://en.wikipedia.org/wiki/Finite_element_method" target="_blank">FEA</a> package, or run some new geometry in <a href="https://en.wikipedia.org/wiki/Computational_fluid_dynamics" target="_blank">CFD</a> &mdash; then it&#39;s almost certain that FORTRAN is involved in that stack somewhere, even six or seven decades on.</p><hr><h2 id="isPasted">Why Software Matters For Hardware</h2><p>The advantages of using digital computers in the physical engineering disciplines are both marked and multifarious, and I hope somewhat obvious.</p><p>At a low level, these advantages can be quite straightforward. They include things like providing engineers with the ability to perform large numbers of calculations with little to no risk of human error. Think of something like repeated matrix multiplication, perhaps as part of a kinematics problem. How confident would you be in getting every step of that calculation right, without accidentally picking up a value one row down or one column across from where you intended? Thankfully, with the help of a few lines of FORTRAN, MATLAB, or Python, the computer can perform these calculations on your behalf, perhaps billions of times, without the threat of inconsistency or human error. At least... <a href="https://en.wikipedia.org/wiki/Pentium_FDIV_bug" target="_blank"><em>almost</em> all of the time.</a></p><p>At a higher level, however, these advantages are even more pronounced. They are the culmination of decades of work across several disciplines; the output of engineers, scientists and mathematicians who have designed, implemented, and shared computational techniques that address a myriad of challenging problems &mdash; from solving partial differential equations at scale, enabling modern FEA and CFD, through to the development of boundary representation solid modelling techniques that enable modern CAD systems. The convergence of these technologies has revolutionised engineering; accelerating development cycles, reducing the need for physical testing, and helping the world&#39;s engineers improve the quality and performance of everything from ships to spacecraft.</p><p>As an illustration, think of the draughting process. For many engineering professionals who started their careers prior to the 1990s, draughting work would have still involved a drawing board, pens, and paper, cellulose, and/or linen. In the event of a mistake, the draughtsperson would have had to break out the scalpel or Tipp-Ex to &#39;erase&#39; the error, or issue an entirely separate drawing amendment to be paired with the original documents. With modern CAD systems, minor drawing modifications completed prior to release go completely unnoticed, and subsequent amendments can be as simple as a quick tweak and a brief change order &mdash; no redrawing of anything, no folder of amendments.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg1NTI3MDQ5MjY0LUhXRHJhd2luZ09mZmljZS5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib"><span class="fr-inner"></span></span></span></p><figcaption id="isPasted">The Harland &amp; Wolff Drawing Office, Belfast, at some point during the 1960s or 1970s. My father served part of his apprenticeship in this room a decade or two later.<br><em>Maritime Belfast/National Museums Northern Ireland</em></figcaption><p>Better yet, imagine the difference in resource requirement and development cycle times for something like a turbocharger. If every minor tweak to blade or housing geometry requires an entire manufacture, assembly, and balancing routine before the new design makes it to the gas stand, every iteration will fall weeks behind and cost significantly more to explore than its simulation-driven counterpart &mdash; where a design change could make it to CFD in a matter of minutes. This is before we even consider the introduction of something like mathematical optimisation into this loop, where the responsible engineer could define their objective function, their variables, and their constraints, and conceivably let the computer play tunes with the geometry, feed that into the simulation, interrogate the results, then rinse and repeat until it reaches an optimum. Ultimately, well correlated models, <a href="https://en.wikipedia.org/wiki/All_models_are_wrong" id="isPasted" target="_blank">even if wrong</a>, are incredibly useful.</p><hr><h2 id="isPasted">Why Hardware Matters for Everyone</h2><p>So computers are wonderful tools, and so are the niche software packages we run on them, but so what? Well, I believe we now find ourselves at a critical point in both the trajectory of engineering as a discipline, and in its utility as a mechanism for improving our lives as a species. We have a lot of *very* active engineering fields right now, with a lot of important problems to address &mdash; and in the end, at some point, somebody has to actually make stuff to make things better.</p><p>For example, it seems to me that we are standing but a few feet forward of the starting line in a new, privately funded space race. We have seen SpaceX engineers repeatedly achieve nothing short of a triumph of control engineering: reversing a rocket onto a barge in the ocean, opening the door to reuse, and as a result, both dramatically reduced space access costs and dramatically increased launch cadence. We have companies like SpinLaunch working to yield orbital access mechanisms that help escape <a href="https://www.nasa.gov/mission_pages/station/expeditions/expedition30/tryanny.html" target="_blank">the tyranny of the rocket equation&nbsp;</a>.</p><p>We have a burgeoning paradigm shift in personal transport, with electric cars, scooters, and e-bikes encroaching further and further on the internal combustion engine&#39;s long-established territory.</p><p>We have additive manufacturing evolving into a serious, cost-competitive manufacturing technique that can achieve geometries beyond the reach of traditional subtractive approaches, and we have generative design technology helping engineers produce mathematically optimised, often organic-looking solutions to pressing design problems. To see the power of these two in concert, just look at <a href="https://www.czinger.com/" target="_blank">Czinger</a> and <a href="https://www.divergent3d.com/" target="_blank">Divergent</a>.</p><p>And, of course, we have ever more compelling incentives to overhaul the majority of our civilisation&#39;s energy infrastructure, pushing us to diversify the means by which we generate electricity, fostering the development of improved wind, solar, and &mdash; I hope &mdash; nuclear technologies.</p><p>In short, we&#39;ve got a lot of real-world engineering to tackle, and plenty of hardware to build.</p><hr><h2 id="isPasted">Software is Eating the World</h2><p>Ultimately, it strikes me that the way in which we develop the physical systems around us is changing, and not all our tools yet accommodate this.</p><p>Despite the arrival of an entire range of engineering software systems in the latter part of the last century, these were typically specialised products, often designed to form improved but effectively like-for-like replacements of an existing way of accomplishing a given task, whether that was replacing the draughtsman and his drawing board with a CAD workstation, or replacing a complex, <a href="https://twitter.com/CalumDouglas1/status/1550594001874698246" target="_blank">stiffness-altering oil pressure measurement apparatus&nbsp;</a>around a bearing with a CFD case.</p><p>As a result, we find ourselves at a point where, as a profession, we&#39;re equipped with some extremely impressive tools; tools that can help us build the shorter iterative loops that are required to yield accelerated system development.</p><p>However, it feels like we&#39;re still using these revolutionary tools, developing the technology that is critical to our species&#39; future, inside an operational framework that was designed for slide rules, drawing boards, and row after row of draughtsmen packing a tobacco smoke filled drawing office.</p><p>Our systems are heavily siloed, our teams often fragmented. We struggle to collaborate across teams, sites, and time zones. We have vast and unstructured knowledge bases that are full of effectively undiscoverable data, put beyond the reach of those who don&#39;t know exactly where to look. And we struggle for visibility &mdash; of tasks, responsibilities, upcoming changes, and most importantly, of our data.</p><p>Even the big players recognise this. Dassault push <a href="https://discover.3ds.com/reduce-non-value-added-work" target="_blank">research</a> that shows engineers spend 34% of their time performing &#39;non-value-added&#39; work; dominated by the amount of time spent just looking for stuff, trawling through network drives and cluttered inboxes. This same research shows that engineers are typically working with outdated information 20% of the time &mdash; imagine a full day a week, every week, for every member of your team, just being thrown away because of bad input data. This pattern has to change.</p><p>In fact, for newer hardware companies, I think some things have *already* changed. <a href="https://a16z.com/2011/08/20/why-software-is-eating-the-world" target="_blank">Software is eating the world&nbsp;</a>applies not only to the electronic control of fuel injectors and ignition coils, but to the adoption of software-like management practices, with a focus on short design-build-test loops. I may hate the confusing, jargon-heavy nomenclature of &#39;agile&#39; methodology, but there is some method in the madness.</p><hr><h2 id="isPasted">Better, More Collaborative Software for Engineering Teams</h2><p>The future that I see is one where we not only have specialised tools for specific parts of the engineering cycle, but we have a central, engineering specific hub; an operating system that ties these things together.</p><p>We want to see the world&#39;s physical engineering teams build better products faster; to achieve shorter development cycles, to iterate faster, and to better leverage their internal knowledge &mdash; so that they can achieve their goals, whether that&#39;s kinetic launch, faster cars, or greener energy.</p><p>We think this will be brought about by transforming how hardware teams communicate, collaborate, and manage technical knowledge. That means better, more connected software; software that focuses on resolving the productivity-killing shortfalls in current engineering process. We think hardware teams should have technical communication mechanisms that are fast, painless, and traceable; that allow new hires to find the information they need, without requiring someone else to go rooting through their inbox for half an hour. We think engineers should be able to see what colleagues are working on, how these items are prioritised, and what&#39;s coming next. We think engineers should be able to annotate drawings, share calculations and schematics, and offer opinions on proposed solutions &mdash; all in real-time, wherever they are, with all that data stored and indexed for easy discoverability down the line.&nbsp;</p><p>Above all, we think engineers should have a single source of truth to work with; a repository of version-controlled design values, simulation outputs, and performance targets &mdash; whether those are scalar values, or n-dimensional maps.</p><p>In the end, what we want to see is engineering teams moving beyond the pain of e-mail ping-pong with spreadsheets and presentations, to stop scrawling performance differentiating IP over paper drawings that are destined for the shredder, and to start letting the computers handle the busywork.</p>

Betty Jean Jennings and Fran Bilas at the main ENIAC control panel. Moore School of Electrical Engineering. U.S. Army, ARL Technical Library Archives

The recent history of our physical engineering disciplines is intertwined with computing. The future is too. 

The Future of Hardware is... Better Software

<p>In this episode, we delve into a game-changing approach developed by the University of Michigan, where 'simulated terrible drivers' in a mixed reality environment are used to drastically cut the time and cost of Autonomous Vehicle (AV) safety testing. Discover how this innovative method enhances testing by focusing on rare but critical driving events, potentially accelerating the deployment and public acceptance of self-driving cars.&nbsp;</p><p><span class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable" contenteditable="false" draggable="true"><iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/4e11e15a-7cd7-4ab1-a302-e4db87801132?dark=false" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span></p><hr><p>This podcast is sponsored by&nbsp;<a href="https://www2.mouser.com/" id="isPasted" rel="nofollow" target="_blank">Mouser Electronics</a>.&nbsp;</p><p id="isPasted"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg1NTgzMDIxMzU3LU1vdXNlci1zcG9uc29yLTEtYS5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib"></p><hr><p><span style="background-color: transparent;">(3:49) -&nbsp;</span>Simulated terrible drivers cut the time and cost of AV testing by a factor of one thousand</p><p>This episode was brought to you by Mouser, our favorite place to get electronics parts for any project, whether it be a hobby at home or a prototype for work. Click <a href="https://resources.mouser.com/automotive/autonomous-vehicles-the-way-forward" target="_blank">HERE</a> to learn about how electronics suppliers are planning to meet the technical and regulatory requirements to make autonomous vehicles possible!</p><hr><p id="isPasted">As always, you can find these and other interesting &amp; impactful engineering articles on Wevolver.com.</p><p>To learn more about this show, please visit our<a href="https://www.wevolver.com/profile/the.next.byte" target="_blank">&nbsp;shows page</a>. By following the page, you will get automatic updates by email when a new show is published. Be sure to give us a follow and review on<a href="https://podcasts.apple.com/nl/podcast/the-next-byte/id1548255861?l=en" target="_blank">&nbsp;Apple</a> podcasts,<a href="https://open.spotify.com/show/6lPPsRtegnivsRUEsQ09R7?si=IPMiah7xT_apJtPjqvXMlA" target="_blank">&nbsp;Spotify</a>, and most of your favorite podcast platforms!</p>

Xintao Yan and Haojie Zhu, both civil engineering PhD students, prepare to take one of the autonomous vehicles for a test run at Mcity. Photo: Brenda Ahearn/Michigan Engineering

In this episode, we delve into a game-changing approach developed by the University of Michigan, where 'simulated terrible drivers' in a mixed reality environment are used to drastically cut the time and cost of Autonomous Vehicle (AV) safety testing.

Podcast: Augmented Bad Drivers Turbocharge Autonomous Vehicle Development

<p id="isPasted">The&nbsp;<a href="https://www.dlr.de/en/research-and-transfer/research-infrastructure/research-aircraft-fleet/istar-dassault-falcon-2000lx-d-bdlr" target="_blank">In-flight Systems and Technology Airborne Research (ISTAR)</a> aircraft has begun scientific operations. ISTAR flights focus on the flight-mechanical and flight-dynamic characteristics of the modified Dassault Falcon 2000LX. Novel sensors measure how the aircraft behaves during specific flight manoeuvres. The German Aerospace Center (Deutsches Zentrum f&uuml;r Luft- und Raumfahrt; DLR) will use the results to further develop ISTAR&rsquo;s digital twin. This will lead to more energy-efficient aircraft designs. The digital twin becomes an exact virtual image of the aircraft in a computer.</p><p>The flight manoeuvres and ground tests bring together data from novel measurement techniques with state-of-the-art simulation tools. &quot;The digital twin will make virtual flight tests possible in the future,&quot; explains project manager Heiko von Geyr from the&nbsp;<a href="https://www.dlr.de/en/as" target="_blank">DLR Institute of Aerodynamics and Flow Technology</a> in&nbsp;<a href="https://www.dlr.de/en/dlr/locations-and-offices/braunschweig" target="_blank">Braunschweig</a>. ISTAR should not be limited to testing solely within a flight simulator; instead, it should be possible for the aircraft to undergo realistic testing. The aim of the&nbsp;<a href="https://www.dlr.de/as/en/desktopdefault.aspx/tabid-18236/28992_read-76291/" rel="noopener noreferrer" target="_blank">High-speed inflight validation (HighFly)</a> project is to combine several processes: &quot;This enables us to predict the structural deformation, the airflow and the accelerations at any point on ISTAR at any given time,&quot; explains Geyr. Until the researchers can realistically map the aircraft&rsquo;s behaviour digitally, they have to compare the computational models with measurement results and develop them further. &quot;Only then will we be able to determine whether an aircraft behaves exactly the same on the computer as it does under real conditions.&quot;</p><p>ISTAR has been taking off from the airport at DLR&rsquo;s Braunschweig site for measurement flights since the end of February 2023. In Braunschweig, the aircraft was modified for scientific purposes and prepared for the project. Almost 400 manoeuvres have been flown so far at different speeds, altitudes and in different environments in a reserved airspace over Mecklenburg-Western Pomerania. In the future, researchers will be able to test new aircraft designs digitally at a relatively early stage using a digital twin. This will also enable them to assess whether changes affect the aircraft characteristics and flight behaviour. In addition to virtual flight tests, the researchers are also working towards simulation-based certification in the long term. This could accelerate time-consuming and cost-intensive approval procedures in aeronautics.</p><h2>HighFly project investigates changes during flight manoeuvres</h2><p>To measure how the airflow on the wings changes during different flight manoeuvres and how much the wings deform during flight, the researchers attached a dotted foil on the left wing. Up close, some of the dots are round, and some have an elongated shape. Looking through the cameras inside the fuselage pointing at the wing from a certain angle, all the dots appear the same. When the wings bend during flight, the cameras record the change in position of the dots. This is how the local deformation of the wing is determined.</p><p id="isPasted">Sensors are distributed on and under the right wing. These Microelectromechanical Systems (MEMS) sensors measure the pressure and temperature distribution with high accuracy and high frequency during the flight tests. &ldquo;The MEMS system is a completely new development within the project and is being used here for the first time in the transonic speed range under real flight conditions,&rdquo; says Geyr. In the transonic range, the aircraft flies slower than the speed of sound, but the airflow around the aerofoil still reaches supersonic speed in places. This results in a very complex flow with compression shocks, which must be reliably detected by the MEMS sensors.</p><p>In addition, three MEMS sensor surfaces are installed near the flight deck. They measure pressure fluctuations in the thin boundary layers, which are created here by the shape of the aircraft. The DLR Institute of Aerodynamics and Flow Technology in&nbsp;<a href="https://www.dlr.de/en/dlr/locations-and-offices/goettingen" target="_blank">G&ouml;ttingen</a> developed the MEMS sensor surfaces.</p><h2>Valuable database</h2><p>The test pilots fly precisely defined manoeuvres with ISTAR at 11,000, 25,000, 35,000 and 45,000 feet. These correspond to altitudes of 3350, 7620, 10,670 and 13,720 metres. The computers located in the cabin store all the readings. The researchers later compare the treasure trove of data with the results of the&nbsp;<a href="https://www.dlr.de/en/media/videos/2017/video-future-aircraft-engineering-the-numerical-simulation" target="_blank">numerical simulation</a>. Supercomputers, such as DLR&rsquo;s CARO and CARA clusters, are essential for this. &quot;With our measurements, we cover the entire transonic flight range, pushing the boundaries of what can be achieved during flight. The combination of measurement techniques makes this database unique and extremely valuable,&quot; explains Geyr.</p><p id="isPasted">Researchers from the<a href="https://www.dlr.de/en/ft" target="_blank">&nbsp;DLR Institute of Flight Systems</a> have also conducted Parameter Identification (PID) flights as part of the HighFly project. This results in a flight dynamic simulation model of ISTAR. This can then be used in a flight simulator such as the&nbsp;<a href="https://www.dlr.de/en/research-and-transfer/research-infrastructure/air-vehicle-simulator" target="_blank">Air Vehicle Simulator (AVES)</a>. &ldquo;In this way, we can first test newly developed flight control and assistance systems using the simulator before we test them during flight,&rdquo; says Christian Raab from the DLR Institute of Flight Systems.</p><h2>Ground tests for the digital twin</h2><p>On the ground, the researchers from the&nbsp;<a href="https://www.dlr.de/en/ae" target="_blank">DLR Institute of Aeroelasticity</a> conducted a&nbsp;<a href="https://www.dlr.de/en/latest/news/2021/03/20210702_vibrating-research-aircraft" target="_blank">Ground Vibration Test</a> (GVT) and Taxi Vibration Tests (TVT) in the summer of 2021. &quot;The results are relevant far beyond the HighFly project; every modification of ISTAR needs an aeroelastic analysis to demonstrate the operational safety of the modification,&quot; explains Marc B&ouml;swald from the DLR Institute of Aeroelasticity. Aircraft do not have a completely rigid structure. To fly safely, elasticity must therefore be taken into account.</p><p>During the GVT, ISTAR was supported on jacks and subjected to vibrations. More than 200 additional sensors measured ISTAR&rsquo;s reactions, and these were used to create a model. In the TVT, ISTAR was pulled along the runway. Uneven ground causes vibrations via the landing gear. During the current flights, the researchers also monitored the aeroelastic properties of ISTAR using newly developed methods. In future, these tests could also be carried out using the digital twin.</p><p>The DLR Institute of Aerodynamics and Flow Technology also measured the airflow to the engines with&nbsp;<a href="https://www.dlr.de/en/research-and-transfer/research-infrastructure/particle-image-velocimetry-goettingen" target="_blank">Particle Image Velocimetry (PIV)</a> for various thrust settings. The engine inflow influences the aerodynamics of ISTAR. The data obtained has also been made available for the digital twin.</p>

The upgrading of the In-flight Systems and Technology Airborne Research (ISTAR) aircraft will take place in several stages. As an airborne simulator, it is advancing the digitalisation of aeronautics. The research aircraft is based at DLR’s Flight Experiments Facility in Braunschweig, where it is part of the largest civilian research fleet in Europe. Credits: DLR (CC BY-NC-ND 3.0)

ISTAR flights focus on the flight-mechanical and flight-dynamic characteristics of the modified Dassault Falcon 2000LX. Novel sensors measure how the aircraft behaves during specific flight manoeuvres.

Four hundred flight manoeuvres with ISTAR advance its digital twin

<p id="isPasted">The rise of generative AI, as demonstrated by text-based applications like Chat GPT and Bard, as well as image generators like Midjourney and Stable Diffusion, has sparked a wave of anticipation in the engineering realm. The hope is for similar tools to evolve that can assist in 3D design space, helping to tackle complex engineering problems.</p><p>Emerging research on converting text to 2D to 3D point clouds or SDFs to 3D meshes is fueling enthusiasm for shape generation possibilities. But, the journey from creating something that merely &#39;<em>looks</em>&#39; right to devising something that &#39;<em>performs</em>&#39; to meet functional requirements is paved with many challenges. They demand distinct training data, a very approach to specifying requirements to produce forms and a different way of thinking about design.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 709px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MzMwOTUxOTAwLUFEU0sgM0QgQUkgQ0RGQU0ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 707px;" class="fr-fic fr-dib"><span class="fr-inner"><a href="https://arxiv.org/abs/2110.02624" id="isPasted" target="_blank">Sanghi, Aditya, et al., Clip-forge: Towards zero-shot text-to-shape generation, <em>CVPR</em> 2022.</a></span></span></span></p><p>The engineering industry is beginning to witness the first AI-integrated commercial software tools suitable for the design and engineering. These include offerings from established software firms like Altair, with a rich history in &#39;simulation-driven design&#39;, and startups like Monolith and PhysicsX working to help engineers to predict high-performance problems while also reducing reliance on simulation in the design iteration process (not the validation process).</p><p>Altair&#39;s <strong>d</strong><strong id="isPasted">esignAI</strong> combines physics-based simulation-driven design and machine learning-based AI-driven design to create high-potential designs earlier in development cycles. While <strong>p</strong><strong id="isPasted">hysicsAI&nbsp;</strong>is used to predict physics outcomes quickly leveraging historical simulation data to deliver physics predictions in a fraction of the time it takes traditional solvers to do the same. PhysicsAI uses geometric deep learning to operate directly on meshes and CAD models while <strong>r</strong><strong>apidMiner</strong>, Altair&rsquo;s data analytics and AI platform enables users to develop data pipelines and machine learning models without needing in-house data science expertise.</p><p><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MzI3MjI2MTIxLUFsdGFpci1QaHlzaWNzQUktQ0RGQU0ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 603px;" class="fr-fic fr-dib"></p><hr><blockquote><p>Capturing and understanding design intent is crucial for developing machine learning models that can predict performance and optimize designs. &nbsp;</p></blockquote><p><span style="color: rgb(66, 66, 66); font-size: 19px; background-color: transparent;"><a href="https://cdfam.com/challenges-of-ai-in-computational-engineering/" rel="noopener noreferrer" target="_blank">Jaideep Bangal - Director of Simulation Driven Design and Manufacturing, Altair</a></span></p><hr><p>While AI&#39;s application in manufacturing for process development, optimization, and quality assurance is being quickly established, the design space still grapples with a lack of rich metadata for training ML algorithms. The use of simulation to synthesize training data, despite its computational expense, provides the prospect of exploring solutions beyond the realm of known possibilities.</p><p>Evaluating generative models poses significant challenges, often necessitating a comparison of the distribution of generated designs with training data. This merely confirms if the produced design aligns with the training data.&nbsp;</p><p>Including simulation in the training data and workflow offers the potential for a more objective form of synthesis and evaluation. It may help uncover designs that, despite differing from the training data, perform well. &nbsp;</p><p><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1684327021114-learning+to+generate+shaped+CDFAM+Autodesk.gif" style="width: 300px;" class="fr-fic fr-dib"></p><hr><blockquote><p>Metrics for evaluating generative models are very tricky and often come down to comparing the distribution of generated designs with the training data. This only tells us if the generated design is like the training data.<br id="isPasted">But simulation allows us to evaluate more objectively, so we might be able to find designs that are unlike the training data but perform well, i.e. the network produces something that&rsquo;s both novel and useful.</p></blockquote><p><a href="https://www.linkedin.com/in/karldd/" rel="noreferrer noopener" target="_blank" id="isPasted"></a><a href="https://cdfam.com/learning-to-generate-shapes-ai-autodesk/" target="_blank" rel="noopener noreferrer"></a><a href="https://cdfam.com/learning-to-generate-shapes-ai-autodesk/" rel="noopener noreferrer" target="_blank">Karl D.D. Willis Senior Research Manager, Autodesk Research AI Lab.</a></p><hr><p>Simulation, despite its promising potential in exploring novel solutions outside of the training data, can carry a hefty computational price tag.&nbsp;</p><p>Before the investment in establishing the dataset and training begins to pay dividends, a large number of optimization problems may need to be solved to provide solutions. Academic literature shows that, in the most favorable scenarios, this break-even number is in the thousands. In less favorable instances, it can reach hundreds of thousands, even for problems with a mere few thousand design variables.</p><p id="isPasted">These approaches often lack generality, meaning their effectiveness is restricted to limited applications, number of boundary conditions and other parameters. This limitation further complicates their implementation in real-world scenarios, as engineers must contend with a diverse array of conditions when designing and optimizing products.</p><p>Striking the right balance between leveraging simulation for exploring novel solutions and managing computational costs and constraints is crucial in the pursuit of AI-driven engineering advances. The development of more efficient and adaptable simulation techniques, alongside the intelligent integration of AI, will be key in overcoming these challenges and maximizing the potential of AI-assisted engineering solutions.</p><p><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MzMxMDU5MTcyLUNERkFNIHRvcCBvcC5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 645px;" class="fr-fic fr-dib"></p><hr><blockquote><p id="isPasted">High resolution is required either to resolve local field properties like stress concentrations or to allow optimization of huge structures like airplane wings or bridge decks.&nbsp;</p><p>One finite element evaluation may take hours on super computers making it infeasible to generate data enough for training an ANN.&nbsp;</p></blockquote><p><a href="https://cdfam.com/is-ai-driven-design-a-computational-dead-end-in-engineering/" rel="noopener noreferrer" target="_blank">Prof. Ole Sigmund TopOpt Group - Technical University of Denmark</a></p><hr><p><br id="isPasted">In the manufacturing domain, proprietary design, simulation, and manufacturing data is often considered the &quot;secret sauce&quot; of each company. This proprietary information is closely guarded, sometimes to the extent that it&#39;s not even shared internally.</p><p>Amassing a warehouse of meaningful data, both within and beyond these corporate &quot;walled gardens,&quot; poses a formidable challenge. This protective stance towards data not only limits the potential for broad collaborative efforts, but also hampers the comprehensive training of machine learning algorithms, potentially stymieing the evolution of AI in engineering.&nbsp;</p><p>It underscores the need for new strategies and <a href="https://3mf.io/" rel="noopener noreferrer" target="_blank">protocols that encourage data sharing</a> while preserving intellectual property and competitive advantage.</p><p>Most companies looking to integrate AI into their engineering workflow are at this very earliest, data collection and distillation phase.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 527px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MzI3MTExNDk5LUFJLUhpZXJhcmNoeS1vZi1uZWVkcy1DREZBTS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 525px;" class="fr-fic fr-dib"><span class="fr-inner">&nbsp;<a href="https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007" id="isPasted" target="_blank">&ldquo;AI Hierarchy of Needs&rdquo;</a> by Monica Rogati.</span></span></span></p><hr><blockquote><p>We all want to get to the top of the pyramid &ndash; deploying powerful AI models that revolutionize our businesses. However, many of us are still at the base of that pyramid, figuring out how to effectively log and clean data.</p></blockquote><p><a href="https://cdfam.com/design-for-artificial-intelligence-dfai/" rel="noopener noreferrer" target="_blank">Chris McComb &ndash; Director, Human+AI Initiative -Carnegie Mellon University</a></p><hr><p>While many researchers are focusing on the task of integrating AI into engineering, addressing a multitude of data-related challenges. Chris McComb and his team at Carnegie Mellon University are taking a broader approach by examining not only the mechanical processes and individual approaches towards Design for Artificial Intelligence (DfAI), but also the dynamics of team-based collaboration.&nbsp;</p><p>Their research delves into understanding the subtleties of how engineers interact with AI-enabled engineering software and, just as crucially, their interpersonal communication within this context.&nbsp;</p><p>By exploring both the technical and human aspects, their work aims to pave the way for more efficient and effective AI integration in the engineering process, fostering innovation and breakthroughs in the field.</p><p><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MzMxMjc0MDU0LWRmYWktQ0RGQU0gaW1hZ2UucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 720px;" class="fr-fic fr-dib"></p><hr><blockquote><p>One thing that I encourage people to keep in mind when working with AI is that we are not Teflon &ndash; just as we influence AI tools, the tools also influence us. Exactly how AI influences us depends on how we work with it, though.</p></blockquote><p><a id="isPasted" target="_blank">Chris McComb &ndash; Director, Human+AI Initiative -Carnegie Mellon University</a></p><hr><p id="isPasted">The utilization of AI as a tool prompts a shift in our focus, moving away from basic modeling operations and towards activities of higher value such as data distillation and problem framing.&nbsp;</p><p>By integrating AI as an equal collaborator within our teams, we foster a hybrid human/machine work environment that has the potential to outperform purely human teams.</p><p>However, while it might be tempting to believe that AI is an unwavering ally, it is important to note that it is not always beneficial. Much like any tool, AI&#39;s effectiveness is contingent on its appropriate application and use within the given context.</p><p><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MzMyMDIzOTI4LUFJLXRlYW0tcGVyZm9ybWFuY2UtQ0RGQU0ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 579px;" class="fr-fic fr-dib"></p><p>Chris and his team developed an AI Designer that surpassed its human counterparts in engineering performance.&nbsp;</p><p>However, <a href="https://www.sciencedirect.com/science/article/pii/S0142694X21000016" rel="noopener noreferrer" target="_blank">mixed teams consisting of both human engineers and an AI designer didn&#39;t always fare as well</a> as the individual AI or human entities.&nbsp;</p><p>This underscores the potential of AI in enhancing design, while simultaneously revealing potential risks if blindly trusting results from a black box system.&nbsp;</p><p>As we embrace this new era, it&#39;s crucial to consider both the opportunities and challenges that integrating AI into our engineering practices presents.</p><hr><p>Chris McComb, Karl D.D. Willis and Jaideep Bangal will be presenting alongside other experts in the field of computational design, engineering and AI at <a href="https://cdfam.com/" rel="noopener noreferrer" target="_blank">CDFAM Computational Design (+DfAM) Symposium</a> in NYC June 14-15.</p>

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