Engineering-informed Machine Learning for Smart Manufacturing - An Analytical Foundation for Optimal Compensation of Three-Dimensional Shape Deviations in Additive Manufacturing
Abstract:
Compensation or adjustment of product designs is one viable approach for accuracy control in additive manufacturing. This talk provides an analytical foundation to achieve optimal compensation for high-precision AM. We first present the optimal compensation policy or the optimal amount of compensation for 2D shape deviation. By analyzing its optimality property, we propose the minimum area deviation (MAD) criterion to offset 2D shape deviations. This result is then generalized by establishing the minimum volume deviation (MVD) criterion and by deriving the optimal amount of compensation for 3D shape deviations. Furthermore, MAD and MVD criteria provide convenient quality measure or quality index for AM built products that facilitate online monitoring and feedback control of shape geometric accuracy.
Biography:
Dr. Qiang S. Huang is a Professor at the Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California (USC), Los Angeles. He is currently a visiting Professor at SMMG. His research focuses on Machine Learning for Additive Manufacturing (ML4AM) and quality control theory and methods 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.