Shape deviation models constitute an important component in quality control for additive manufacturing (AM) systems. However, specified models have a limited scope of application across the vast spectrum of processes in a system that are characterized by different settings of process variables, including lurking variables. In this paper, which was recently accepted by the Annals of Applied Statistics, Dr. Arman Sabbaghi and Dr. Qiang Huang present a new effect equivalence framework and Bayesian method that enables deviation model transfer across processes in an AM system with limited experimental runs. Model transfer is performed via inference on the equivalent effects of lurking variables in terms of an observed factor whose effect has been modeled under a previously learned process. Studies on stereolithography illustrate the ability of their framework to broaden both the scope of deviation models and the comprehensive understanding of AM systems. More details about this paper can be found at the following link:
