ISE Seminar: Infer and Control Effects of Lurking Variables for 3D Printing Quality Control

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Qiang Huang, Professor
Epstein Department of Industrial and Systems Engineering
University of Southern California

In physical experiments, the system or process response can be affected by three categories of variables: experimental variables or factors to be investigated, observable variables maintained to be constant, and lurking variables that are unknown or unmeasurable. The lurking variables have been assumed to be constant and independent of other variables without verification. This talk presents a rigorous formulation to infer and control the effects of lurking variables through an effect equivalence approach. Hypothesis testing can be conducted to test the possible violation of the assumption. Application in 3D printing accuracy control is demonstrated.

Short Bio: 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 is a fellow of the Institute of Industrial and Systems Engineers, a fellow of the American Society of Mechanical Engineers (ASME), and a senior member of the National Academy of Inventors. He served as a Department Editor for IISE Transactions and an Associate Editor for ASME Transactions, Journal of Manufacturing Science and Engineering, among others. He is the General Chair of 2025 IEEE Conference on Automation Science and Engineering at Los Angeles, California.