Part qualification is the most critical task in any factory. Manufactured parts must be verified as having expected geometry and being free of defects. For production additive manufacturing (AM), part qualification is particularly challenging and costly. AM parts often have complex geometries that can be difficult to measure. In addition, AM processes typically have geometric distortions and defect rates that are strongly affected by part geometry. These challenges require new measurement strategies that are automated and scalable.
This talk describes research on metrology automation to measure and analyze parts made with AM. The research collects vision information from AM parts including photographs, high resolution 2D or 3D scans, and X-ray computed tomography (CT). These images are used to train machine learning algorithms that accurately recognize defects, measure part dimensions, and predict part tolerances. This scalable measurement automation allows us to collect large numbers of part measurements far beyond what is possible with conventional methods, enabling new insights about the AM production environment. Our work is conducted in collaboration with a large commercial AM factory using production machines and engineering materials, allowing us to study large data sets and real-world conditions directly applicable for scalable manufacturing.
William P. King, Ph.D. is Professor and Ralph A. Andersen Endowed Chair in the Grainger College of Engineering, Department of Mechanical Science and Engineering, University of Illinois Urbana-Champaign. He holds courtesy appointments in Electrical and Computer Engineering and Materials Science and Engineering. He is the founder of three companies including Fast Radius Inc., which operates one of the world’s largest additive manufacturing factories and was recognized by the World Economic Forum for its technical achievements. He is a Fellow of ASME, AAAS, APS, IEEE, SME, and the National Academy of Inventors. He is the winner of the Gustus-Larson Award and the Ennor Award from ASME.