Quality control of geometric shape deviation in additive manufacturing relies on statistical deviation models. However, resource constraints limit the manufacture of test shapes, and consequently impede the specification of deviation models for new shape varieties. In this paper, which was recently accepted by Technometrics, Dr. Arman Sabbaghi, Dr. Qiang Huang, and Dr. Tirthankar Dasgupta present an adaptive Bayesian methodology that effectively combines in-plane deviation data and models for a small sample of previously manufactured, disparate shapes to aid in the model specification of in-plane deviation for a broad class of new shapes. The power and simplicity of this general methodology is demonstrated with illustrative case studies on in-plane deviation modeling for polygons and straight edges in free-form shapes using only data and models for cylinders and a single regular pentagon. Their new Bayesian approach facilitates deviation modeling in general, and thereby can help advance additive manufacturing as a high-quality technology. More details about this paper can be found at the following link:
