讲座题目：Engineering-Informed Machine Learning for Additive Manufacturing Accuracy Control

报 告 人：Qiang Huang教授 美国南加州大学

报告时间：2023年4月11日（周二）10:00

报告地点：广州国际校区D1-b110

欢迎广大师生积极参与。

吴贤铭智能工程学院

2023年4月6日

报告摘要：Rapid technology advances in additive manufacturing (AM) have been significantly expanding manufacturing capability and utility. One pivotal technological challenge is how to ensure AM product quality and reduce the number of iterative runs. Quality control (QC) in AM currently relies heavily on human expertise and intervention due to product and process complexities. Physical modeling and simulation of AM are still computationally prohibitive for timely QC. Applying popular AI techniques to automate QC not only demands large amounts of costly AM data but also falls short of gaining engineering insights for knowledge generalization and adaptation. This talk discusses engineering-informed machine learning research for AM accuracy control: (1) domain-informed convolution modeling and learning of layer-by-layer fabrication for shape accuracy prediction; (2) optimal compensation of 3D shape deviation; and (3) engineering-informed transfer learning based on effect equivalence.

报告人简介：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.