Aug 8, 2022

GIE Media’s Manufacturing Group IMTS 2022 Conference: The EV Revolution is Charging Up with Additive Manufacturing Learn about the technology influencing today’s electric vehicles. August 8, 2022 About the presentation Wherever you find rapid change and complexity in manufacturing, you’ll find 3D printing where complexity is free and agility is baked in. Automakers are turning to additive manufacturing (AM) to outfit large new electric vehicle (EV) factories and retrofit existing ones, shorten the new vehicle design process, and accommodate shorter production runs and mass customization. This presentation will outline how 3D design methods, advances in industrial-scale printing, and Industry 4.0-connected models are helping both existing and upstart vehicle makers to rise to the EV challenge. Registration Powered by AMT and managed by GIE Media,  The IMTS 2022 Conference  features 69 different sessions you won’t want to miss so  register today . Focused on a range of topics that include process innovation, plant operations, quality/inspection, and automation,  The IMTS 2022 Conference  addresses improving productivity; improving part quality; and developing a stable, competent workforce to lower the cost of manufacturing in the United States and create new levels of market demand. Meet your presenter Fadi Abro joined Stratasys in 2010. He developed extensive knowledge in AM applications for the transportation industry. Abro now leads the automotive vertical for Stratasys in the Americas. He manages a cross-functional team of sales and engineering staff that support automotive market leaders’ implementation of AM. About the company Stratasys creates additive technology solutions for industries including aerospace, automotive, consumer products, design, education, and healthcare. For more than 30 years, a deep and ongoing focus on customers’ business requirements has fueled purposeful innovations that create new value across product lifecycle processes. The Stratasys 3D printing ecosystem of solutions and expertise works to ensure seamless integration into each customer’s evolving workflow. Fulfilling real-world potential of AM, Stratasys delivers industry-specific applications that accelerate business processes, optimize value chains, and drive business performance improvements for thousands of future-ready leaders. Ernst & Young LLP (EY US) and GE Digital formed an alliance to help organizations transform their manufacturing operations. The alliance combines the proven sector excellence and technology consulting services of EY US with the more than 100 years of manufacturing experience and rich digital technology of GE. With the rapidly growing demand for modernized manufacturing systems, companies are seeking to gain a competitive advantage by expediting their digital transformation journey. As critical key performance indicators (KPIs) for the industry include the need for operational efficiency, quality management improvement, loss reduction and energy efficiency, the EY−GE Digital Alliance will provide solutions and guidance on ways to increase productivity while lowering costs. GE Digital is an industry leader in developing software for manufacturing. The business helps foster deeper integration between the enterprise resource planning layer of an organization's technology landscape with its equipment on the shop floor. GE Digital's software tools can be fit to meet the various challenges accompanying the growing manufacturing system market, including improving quality, uptime and operations performance. The manufacturing technology service of EY US, which has been growing over the last 20 years, has proven experience implementing GE Digital software tools for more than 40 different clients spanning various industrial sectors. Through this alliance, manufacturers can also leverage support from EY US teams to implement and integrate GE Digital software to develop more data-driven manufacturing operations. Scott Dixon, digital manufacturing leader, Ernst & Young LLP, says, "I am excited to see EY US and GE Digital joining forces to help manufacturers achieve their business outcomes building a fit-for-purpose enterprise manufacturing technology landscape. The EY−GE Digital Alliance will harness the combined power of technology, services and solutions, industry insights, system integration and consulting experience of the two organizations to optimize the value chain for companies throughout the industry." Richard Kenedi, GE Digital GM, Manufacturing and Digital Plant, says, "Software is mission-critical to accelerating transformation in industry today. The alliance brings together leaders in software solutions and technology services to help industrial companies solve their toughest challenges. Software supports corporate initiatives including continuous improvement, compliance, sustainability and global competitiveness. Companies want those solutions to be working quickly and efficiently so they can see fast time to value. Working with EY US creates that synergy needed to pursue business opportunities." Presenters: Cory Raizor is SCHUNK ’s Business Development Manager, Automated Machine Tending, Automation & Gripping Systems. With nearly 10 years of experience in automation sales, Raizor is a rising expert in the robotics and end-of-arm-tooling industries. Cory joined SCHUNK as a territory manager before transitioning to a focused account-based sales manager role. Cory now serves as a business development manager, leading SCHUNK USA’s Collaborative Accessories Program, a national distribution channel focused on collaborative robot solution providers. Gary Labadie, Global Product Director of Automation products for DESTACO , is representing Reid Supply Co. on this panel. Labadie has been with DESTACO for 12 years, but in the industry for 35+ years supporting product development and product management. He’s an expert on the topic of collaborative robots (cobots). Time: August 17, 2022 12:00 PM in Eastern Time (US and Canada) Please join us for this insightful webinar. Registration is free and once registered, you will receive a link post-event to watch on-demand. New materials with unique properties that can be used for 3D printing are always under development, however figuring out how to print with these materials can be complex. Often, an expert operator must use manual trial-and-error to determine ideal parameters that consistently print a new material effectively. These parameters include printing speed and how much material the printer deposits. MIT researchers have now used artificial intelligence (AI) to streamline this procedure, developing a machine-learning (ML) system that uses computer vision to watch the manufacturing process and then correct errors in real-time. The researchers used simulations to teach a neural network how to adjust printing parameters to minimize error, and then applied that controller to a real 3D printer. Their system printed objects more accurately than all the other 3D printing controllers they compared it to. The work avoids the prohibitively expensive process of printing thousands or millions of real objects to train the neural network. And it could enable engineers to incorporate novel materials more easily into their prints, which could help develop objects with special electrical or chemical properties. It could also help technicians adjust the printing process on-the-fly if material or environmental conditions change unexpectedly. “This project is really the first demonstration of building a manufacturing system that uses machine learning to learn a complex control policy,” says senior author Wojciech Matusik, professor of electrical engineering and computer science at MIT who leads the Computational Design and Fabrication Group (CDFG) within the Computer Science and Artificial Intelligence Laboratory (CSAIL). “If you have manufacturing machines that are more intelligent, they can adapt to the changing environment in the workplace in real-time, to improve the yields or the accuracy of the system. You can squeeze more out of the machine.” The co-lead authors on the research are Mike Foshey, a mechanical engineer and project manager in the CDFG, and Michal Piovarci, a postdoc at the Institute of Science and Technology in Austria. MIT co-authors include Jie Xu, a graduate student in electrical engineering and computer science, and Timothy Erps, a former technical associate with the CDFG. Picking parameters Determining the ideal parameters of a digital manufacturing process can be one of the most expensive parts of the process because so much trial-and-error is required. And once a technician finds a combination that works well, those parameters are only ideal for one specific situation. She has little data on how the material will behave in other environments, on different hardware, or if a new batch exhibits different properties. Using a ML system is fraught with challenges, too. First, the researchers needed to measure what was happening on the printer in real-time. To do this, they developed a machine-vision system using two cameras aimed at the nozzle of the 3D printer. The system shines light at material as it is deposited and, based on how much light passes through, calculates the material’s thickness. “You can think of the vision system as a set of eyes watching the process in real-time,” Foshey says. The controller would then process images it receives from the vision system and, based on any error it sees, adjust the feed rate and the direction of the printer. But training a neural network-based controller to understand this manufacturing process is data-intensive and would require making millions of prints. So, the researchers built a simulator instead. Successful simulation To train their controller, they used a process known as reinforcement learning in which the model learns through trial-and-error with a reward. The model was tasked with selecting printing parameters that would create a certain object in a simulated environment. After being shown the expected output, the model was rewarded when the parameters it chose minimized the error between its print and the expected outcome. In this case, an “error” means the model either dispensed too much material, placing it in areas that should have been left open, or did not dispense enough, leaving open spots that should be filled in. As the model performed more simulated prints, it updated its control policy to maximize the reward, becoming more and more accurate. However, the real world is messier than a simulation. In practice, conditions typically change due to slight variations or noise in the printing process. So the researchers created a numerical model that approximates noise from the 3D printer. They used this model to add noise to the simulation, which led to more realistic results. “The interesting thing we found was that, by implementing this noise model, we were able to transfer the control policy that was purely trained in simulation onto hardware without training with any physical experimentation,” Foshey says. “We didn’t need to do any fine-tuning on the actual equipment afterwards.” When they tested the controller, it printed objects more accurately than any other control method they evaluated. It performed especially well at infill printing, which is printing the interior of an object. Some other controllers deposited so much material that the printed object bulged up, but the researchers’ controller adjusted the printing path so the object stayed level. Their control policy can even learn how materials spread after being deposited and adjust parameters accordingly. “We were also able to design control policies that could control for different types of materials on-the-fly. So if you had a manufacturing process out in the field and you wanted to change the material, you wouldn’t have to revalidate the manufacturing process. You could just load the new material and the controller would automatically adjust,” Foshey says. Now that they have shown the effectiveness of this technique for 3D printing, the researchers want to develop controllers for other manufacturing processes. They’d also like to see how the approach can be modified for scenarios where there are multiple layers of material, or multiple materials being printed at once. In addition, their approach assumed each material has a fixed viscosity (“syrupiness”), but a future iteration could use AI to recognize and adjust for viscosity in real-time. Additional co-authors on this work include Vahid Babaei, who leads the Artificial Intelligence Aided Design and Manufacturing Group at the Max Planck Institute; Piotr Didyk, associate professor at the University of Lugano in Switzerland; Szymon Rusinkiewicz, the David M. Siegel ’83 Professor of computer science at Princeton University; and Bernd Bickel, professor at the Institute of Science and Technology in Austria. The work was supported, in part, by the FWF Lise-Meitner program, a European Research Council starting grant, and the U.S. National Science Foundation. About the presentation We look forward to demystifying the insurance world and specifically Product Liability. In addition to developing a better understanding of what product liability is, we’ll touch on key points to consider when buying this insurance coverage. We’ll also share some real-world loss scenarios to bring this teaching to life. We’ll take the audience behind the scenes to better understand the world of insurance with a focus on Product Liability, E&O, and Cyber. Key takeaways will include: • Pricing – a glimpse into how an Underwriter will price your business • Cost Containment – how to control your insurance premiums • Broker selection – how you know you’re with the right broker/agent • Coverages – what do you really need, key areas to focus on • Lessons from losses – how to prevent losses that cause downtime • Carrier services – how to best utilize your carrier’s claims and risk control offerings Registration Powered by AMT and managed by GIE Media,  The IMTS 2022 Conference  features 69 different sessions you won’t want to miss so  register today . Focused on a range of topics that include process innovation, plant operations, quality/inspection, and automation,  The IMTS 2022 Conference  addresses improving productivity; improving part quality; and developing a stable, competent workforce to lower the cost of manufacturing in the United States and create new levels of market demand. Meet your presenter Michael Carroll has more than 30 years of commercial insurance experience and is the leader of the Advanced Manufacturing and Technology practice for Sompo International. In this role he's responsible for developing strategies, insurance products and underwriting teams to service Sompo’s clients across North America, Canada, and Mexico. He also works closely with the Sompo Regional Underwriting Executive team to support distribution management and client relations. About the company Sompo Int’l Holdings Ltd. (Sompo Int’l), a global specialty provider of property and casualty insurance and reinsurance, was established in March 2017 as the result of the acquisition of Endurance Specialty Holdings Ltd. by Sompo Holdings Inc. (Sompo). With a strong commitment to the specialty markets, each of our teams is led and staffed by experienced underwriters, actuaries and claims professionals with deep expertise in the class or line of business in which they specialize, delivering tailored solutions to address our clients’ risk management needs today and as their businesses evolve.