Title: Automated Geometric Qualification of 3D-Printed Products

Abstract:

Geometric qualification of a product is typically performed by specifying features or regions of interest (ROIs) during design, conducting shape registration to establish correspondence between the inspected product and its design counterpart, and measuring discrepancies for compliance assessment. For complex freeform products, the qualification often requires human intervention to ensure accuracy, particularly in personalized manufacturing through 3D printing. However, geometric variety and complexity can induce operator-to-operator variability due to heterogeneous spatial distributions of geometric distortions. To enable automated product qualification, we propose to specify ROIs as surface patches defined by geometric descriptors indicative of intrinsic deviation patterns. Utilizing these descriptors, ROI specification via shape space dimension reduction, non-rigid intrinsic shape registration, and intrinsic deviation representation can therefore be conducted automatically for product qualification. Finite types of ROIs or surface patches can be extracted based on their intrinsic deviation patterns, independent of covariates such as size and location. A software demo has been developed to implement the qualification process.

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, detailed in his monograph "Domain-informed Machine Learning for Smart Manufacturing", has been focusing on machine learning for smart manufacturing and quality control for personalized manufacturing. He is an IISE Fellow Award, ASME Fellow, and a senior member of US National Academy of Inventors. He holds eight patents related to quality control in additive manufacturing.