Automated Geometric Shape Deviation Modeling for Additive Manufacturing Systems via Bayesian Neural Networks

A significant challenge in comprehensive geometric accuracy control of an additive manufacturing (AM) system is the specification of shape deviation models for different computer-aided design products manufactured on its constituent AM processes. Current deviation modeling techniques do not satisfactorily address this challenge because they can require substantial user inputs and efforts to implement. We present a new model building methodology based on a class of Bayesian neural networks (NNs) that directly addresses this challenge with much less effort. Our method enables automated deviation modeling of different shapes and AM processes and yields models with higher predictive accuracies compared to the existing modeling methods on the same samples of manufactured products. A fundamental innovation in our methodology is the design of new and connectable NN structures that facilitate the leveraging of previously specified deviation models for adaptive model building of new shapes and AM processes. The power and broad scope of our method are demonstrated with several case studies on both in-plane and out-of-plane deviations for a wide variety of shapes manufactured under different stereolithography processes. Our Bayesian methodology for automated and comprehensive deviation modeling can ultimately help to advance flexible, efficient, and high-quality manufacturing in an AM system. Note to Practitioners-Additive manufacturing (AM) systems possess an intrinsic capability for one-of-a-kind manufacturing of a vast variety of shapes across a wide spectrum of constituent processes. Learning how to control geometric shape accuracy in a comprehensive manner for an AM system is vital to its operation. This task is challenging due to constraints on the number of test shapes that can be manufactured and user efforts that can be devoted for learning and predicting geometric errors of different sets of shapes and AM processes. This article presents an automated machine learning methodology for comprehensive learning and prediction of geometric errors in an AM system based on a limited number of test shapes manufactured under different processes. Several case studies serve to validate the potential of our methodology to learn effective geometric accuracy control policies for general AM systems in practice.