Digital Transformation Using Artificial Intelligence/Machine Learning

ABSTRACT Co-Hosted by SMTA Puget Sound Chapter Manufacturing has evolved and become more automated, computerized, and complicated. The manufacturing industry aims to improve competitiveness by convergence with cutting-edge information and communication techniques to produce a high-quality product at minimum cost and secure a new growth engine. Due to the cross-functional nature of the manufacturing firms, the enhancement of production planning and control functions can lead to the global improvement of manufacturing systems. Data has recently become a valuable resource, and it is becoming cheaper and more accessible than ever to collect and store along with the rapid development in computing and sensing systems. Such capability can make Artificial Intelligence (AI)/Machine Learning (ML) approach an appealing solution to tackle manufacturing challenges. Along with technical advances toward Industry 4.0, we develop smart manufacturing platforms by adapting advanced AI/ML techniques by accompanying mathematical optimization to enhance the manufacturing industry's production capabilities and qualities. Notably, in this talk, we address the current problems in the manufacturing industry and demonstrate the applications of our smart manufacturing platforms in electronics manufacturing, including a closed-loop qualify control system with dynamic parameter optimization and surrogate modeling based on AI/ML. Specifically, we present data collection/process strategy, experimental design, data-driven approaches based on the advanced AI/ML techniques and mathematical optimization, and validation/verification of the outcomes. Eventually, we will provide examples of how smart manufacturing technologies can be used to monetize manufacturing data. Agenda: - Introduction to Artificial Intelligence, Machine Learning, and Deep Learning - Smart Electronics Manufacturing - Closed-loop Feedback Control: Stencil Printing Process Optimization - Closed-loop Feedback Control: Chip Mounter Process Optimization - Reflow Oven Profile Prediction About the Speakers: Sangwon Yoon, Ph.D.: Prof. Yoon is a recipient of the SUNY Chancellor's Award for Excellence in Scholarship and Creative Activities in 2019 and a highly successful researcher who leads many productive long-term industry collaborations. Prof. Yoon received his doctoral degree in School of Industrial Engineering at Purdue University, and he joined the faculty of the Watson School in the Department of Systems Science and Industrial Engineering at State University of New York at Binghamton in 2010. Prof. Yoon is currently a Professor. In addition, he has a joint title (i.e., Professor) in School of Commerce at Waseda University, Tokyo, Japan and is also a Visiting Professor and a Co-Director of Data Science Center of Excellence at Nirma University, Ahmadabad, India. Prof. Yoon directs the Complex System Design and Analysis Laboratory and is a faculty member of the Watson Institute for Systems Excellence. He leads collaborations with faculty, staff, students, and industry partners, such as such as Analog Devices, Samsung, Sanmina SCI, Koh Young Technology, Raymond, Toyota Material Handling, Xerox, Innovation Associates, United Health Services, Montefiore Medical Center, Bennett Distribution Services, U.S. Xpress, and etc., and has secured close to $7 million from more than 70 industrial projects. Prof. Yoon has been studying how to extract useful insights from expanding data sets to support intelligent decision-making processes. His research not only resides in better understanding large-scale data set by using statistical learning methodologies, but also leverages optimization, soft computing, simulation, and complex theories with conventional machine learning algorithms. As a result, Prof. Yoon has published in over 130 internationally renowned journals and conference proceedings. He was also a member of the Data Science Transdisciplinary Area of Excellence (TAE) initiative and is an active member of the Health Sciences TAE at his institution. Daehan Won, Ph.D.: Prof. Won received a B.S. (2008) and M.S. (2010) in industrial engineering from Korea Advanced Institute of Science and Technology, Daejeon, S. Korea, and a Ph.D. (2016) in industrial and systems engineering from University of Washington, Seattle WA. In 2016, he joined the Department of Systems Science and Industrial Engineering, Binghamton University, SUNY, as an Assistant Professor. His research interests lie in mathematical programming in large-scale programming and data analytics/mining for various healthcare and manufacturing fields. He is recently working on designing new platforms for smart electronics manufacturing systems to cope with advances in industry 4.0. as well as health informatics in biomedical engineering. He has published thirty-five journal and conference papers, including Journal on Computing, IEEE CPMT, IEEE Intelligent Systems, etc. Dr. Won's research has been funded by industry partners such as Koh Young, Analog Devices, and LiveOnNY and national consortiums such as Nano-Bio Manufacturing Consortium (NBMC). He has been serving as multiple session chairs in international conferences, including the Institute Industrial and Systems Engineers (IISE) and the Institute for Operations Research and the Management Sciences (INFORMS) at Health Systems and Optimization for Machine Learning with Large Scale Data, and panel reviewers in National Science Foundation (NSF). He received the best paper award in the Data Analysis and Information System track at the 2018 IISE conference. He was named the finalist at the SAS data mining paper competition in INFORMS 2016. Files Available to Download: • Recorded Presentation (On-Demand) On Demand Webinar