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Peng (Edward) Wang

Associate Professor, Mechanical and Aerospace Engineering
Develops advanced AI/ML tools for smart manufacturing, including predictive maintenance, quality assurance in additive manufacturing, and robotic automation
Office: 449B Glennan

Education

Ph.D., Mechanical & Aerospace Engineering, Case Western Reserve University, 2017
M.S., Computer Science, Beijing University of Chemical Technology, China, 2012
B.S., Communication Engineering, Beijing University of Chemical Technology, China, 2010

Awards and Recognitions

2024, Young Investigator Award, International Symposium on Flexible Automation (ISFA)
2023, CAREER Award, National Science Foundation (NSF)
2023, Best Paper Award, Manufacturing Science and Engineering Conference (MSEC)
2022, Outstanding Young Manufacturing Engineer Award, Society of Manufacturing Engineers (SME)
2021, Outstanding Technical Paper Award, North American Manufacturing Research Conference (NAMRC)
2020, Outstanding Technical Paper Award, North American Manufacturing Research Conference (NAMRC)
2020, Best Paper Award, CIRP Conference on Manufacturing Systems (CMS)
2017, Outstanding Technical Paper Award, North American Manufacturing Research Conference (NAMRC)
2016, First Prize, DMDII DMC Hackathon

Research Interests

1) developing cost-effective edge devices together with data management, processing, and learning pipelines to reduce the barriers for manufacturers in building affordable digitalized and intelligent shop floors. 2) improving AI and ML credibility and generalizability in manufacturing, through integrating domain-knowledge-based constraints to data-driven learning processes and, toward mutual augmentation between data-driven decision-making and physical understandings. 3) reducing demands/efforts on manual data labeling and ML model maintenance, through unsupervised learning from big unlabeled plant data on characterizing machine/process dynamics/uncertainties. 4) advancing in-situ process monitoring (i.e., defect detection and quality prediction) and real-time control of complex manufacturing processes (e.g., metal additive manufacturing). 5) robotic automation of complex manufacturing processes (e.g., welding).

Teaching Interests

Machine Learning for Manufacturing, Control Theories

Professional Leadership and Service

Monday, August 28, 2023 - PRESENT, Co-Chair, Quality and Reliability Technical Committee ASME Manufacturing Engineering Division (MED)
Sunday, January 28, 2018 - PRESENT, Member North American Manufacturing Research Institution of SME (NAMRI/SME) Scientific Committee
Friday, January 28, 2022 - PRESENT, Associate Editor Journal of Intelligent Manufacturing
Thursday, January 28, 2021 - PRESENT, Associate Editor IEEE Sensors Journal
Tuesday, January 28, 2020 - PRESENT, Program Committee and Track Organizer International Symposium on Flexible Automation (ISFA)