How Do We Learn to Use Learning in Manufacturing Systems
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Abstract: Manufacturing has undergone significant changes over the past five-ten years thanks to technological advancements that have been leveraged to meet a diverse set of customer requirements driven by global and societal needs. Conventional manufacturing control strategies were typically designed for robustness and speed within a controlled and well-regulated environment. However, recent demands for customization and agility coupled with big data investments have provided an opportunity for more learning-based methods to be introduced. Data driven strategies have long provided a means of harnessing information to enhance the performance of these complex systems. This talk is motivated by real-world interest from industry in understanding how to combine data-based learning and experiential knowledge to make intelligent decisions that can save time, money, and resources.
In this talk, we examine which aspects of manufacturing processes lend themselves to learning strategies and which bring additional challenges. We also explore cases in which learning has been applied in different ways to additive manufacturing processes in order to improve process knowledge and performance. Opportunities for additional integration of learning into the manufacturing domain will be discussed and open research questions for control-theoretic advancements will be highlighted.
Bio: Kira Barton is an Associate Professor in the Robotics Department and Mechanical Engineering Department at the University of Michigan. She received her B.Sc. in Mechanical Engineering from the University of Colorado at Boulder in 2001, and her M.Sc. and Ph.D. in Mechanical Engineering from the University of Illinois at Urbana-Champaign in 2006 and 2010. She is also serving as the Associate Director for the Automotive Research Center, a University-based U.S. Army Center of Excellence for modeling and simulation of military and civilian ground systems. She was a Miller Faculty Scholar for the University of Michigan from 2017 – 2020. Prof. Barton’s research specializes in advancements in modeling, sensing, and control for applications in smart manufacturing and robotics, with a specialization in learning and multi-agent systems. Kira is the recipient of an NSF CAREER Award in 2014, 2015 SME Outstanding Young Manufacturing Engineer Award, the 2015 University of Illinois, Department of Mechanical Science and Engineering Outstanding Young Alumni Award, the 2016 University of Michigan, Department of Mechanical Engineering Department Achievement Award, and the 2017 ASME Dynamic Systems and Control Young Investigator Award. Kira was named 1 of 25 leaders transforming manufacturing by SME in 2022, and was selected as one of the 2022 winners of the Manufacturing Leadership Award from the Manufacturing Leadership Council.
***Event will take place in hybrid format. The location for in-person attendance will be room 1311 EECS. Attendance will also be possible via Zoom. Zoom link and password will be distributed to the Controls Group e-mail list-serv.
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