In physical experiments, the response of a process or system can be affected by three categories of variables: experimental variables or factors to be investigated, observable variables assumed to be fixed, and lurking variables that are unknown or unmeasurable. The experimental variables are often assumed to be independent of other variables with “constant values”. This talk shows the violation of this assumption in a 3D printing experiment and proposes to infer and control the effects of lurking variables through an effect equivalence approach.

References:

Huang, Q., Zhang, J., Sabbaghi, A., and Dasgupta, T., 2015, “Optimal Offline Compensation of Shape Shrinkage for 3D Printing Processes,” IIE Transactions on Quality and Reliability, Vol. 47, No. 5, pp. 431-441.

Huang, Q., 2016, “An Analytical Foundation for Optimal Compensation of Three-Dimensional Shape Deformation in Additive Manufacturing,” ASME Transactions, Journal of Manufacturing Science and Engineering, Vol. 138(6), 061010.

Sabbaghi, A. and Huang, Q., 2018, “Model Transfer Across Additive Manufacturing Processes via Mean Effect Equivalence of Lurking Variables,” Annals of Applied Statistics, Vol. 12(4), pp. 2409 -2429.

Huang, Q., Wang, Y, Lyu, M., Lin, W., 2020, “Shape Deviation Generator (SDG) – A Con- volution Framework for Learning and Predicting 3D Printing Shape Accuracy, ” IEEE Transactions on Automation Science and Engineering, Vol. 17(3), pp. 1486 -1500.

Huang, Q., 2023, “An Impulse Response Formulation for Small-Sample Learning and Control of Additive Manufacturing Quality, ” IISE Transactions on Design and Manufacturing, Special issue of AI and Machine Learning for Manufacturing, Vol. 55 (9), pp. 926 -939.

Dr. Qiang Huang is a Professor at the Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California (USC), Los Angeles. His research focuses on Domain-informed 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 served as a Department Editor for IISE Transactions and an Associate Editor for ASME Transactions, Journal of Manufacturing Science and Engineering. He is a senior member of US National Academy of Inventors.

For more information about the ESD Seminar, please email esd_invite@sutd.edu.sg