报告人: Prof. Qiang Huang （University of Southern California）

时  间：5月19日 周五 14:30-16:00

主持人：张玺 副教授

地  点：2138cn太阳集团古天乐二教206

Abstract: As a trend of future manufacturing, consumer demand increasingly shifts to personalization, customization, and co-creation. In contrast to mass production, personalized manufacturing faces different challenges and therefore demands new thinking and methods for quality control (QC). This talk first gives a historical perspective on the evolution of QC research and points out the new research challenges and opportunities posed by additive manufacturing (AM). As an initial attempt to address QC challenges in personalized manufacturing, this talk presents engineering-informed machine learning research for AM accuracy control: (1) domain-informed convolution modeling and learning of AM; (2) optimal compensation of 3D shape deviation; and (3) engineering-informed transfer learning method for AM model transfer.

Short Bio: Dr. Qiang Huang is currently a Professor at the Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California (USC), Los Angeles. His recent research focuses on Machine Learning for Additive Manufacturing (ML4AM) and quality control theory for personalized manufacturing. He was the holder of the Gordon S. Marshall Early Career Chair in Engineering at USC from 2012 to 2016. He received IISE Fellow Award, ASME Fellow, NSF CAREER award, the 2021 IEEE CASE Best Conference Paper Award, 2013 IEEE Transactions on Automation Science and Engineering Best Paper Award, among others. He has served as a Department Editor for IISE Transactions and an Associate Editor for ASME Transactions, Journal of Manufacturing Science and Engineering.

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