Ph.D. candidate Yuanxiang Wang and post-doctoral scholar Cesar Ruiz at the USC Daniel J. Epstein Department of Industrial and Systems Engineering received the Best Conference Paper Award in 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE). The presentation video can be found at https://case2021.sciencesconf.org/resource/page/id/37.
Extended Fabrication-Aware Convolution Learning Framework for Predicting 3D Shape Deformation in Additive Manufacturing
Yuanxiang Wang, Cesar Ruiz, Qiang Huang*
Engineering processes such as 3D printing generate complex shape data in the form of 3D point clouds. Qualification and verification of 3D shapes involves modeling and learning of heterogeneous shape deviation data that are affected by both product geometries and process physics. This study develops an engineering-informed, small-sample machine learning methodology to learn and predict deviations of smooth and non-smooth 3D shapes in a unified modeling framework. The generation of a non-smooth 3D shape is mathematically decomposed into the smooth base shape formation and shape difference realization. Both process physics and shape geometries are captured in the learning framework. It provides a new data analytical tool for shape engineering in additive manufacturing and beyond.
