PNNL

Abstract

The advent of additive manufacturing processes brought with it intense re- search into various materials and manufacturing processes. At the same time, the need for validation of material properties, as well as study and forecasting of aging has arisen. Modern imaging techniques like X-ray com- puted tomography are a convenient vehicle for such studies, however, the large data sets they produce require novel analysis techniques to efficiently extract critical information. In this paper, we present our work on developing a 3D extension of the ResNet architecture to distinguish between two build orientations of tensile bars produced by additive manufacturing. Using only information from X-ray computed tomography, our method achieves a 99.3% correct classification at a mis-identification of 1%.