ThermoPore: Predicting Part Porosity Based on Thermal Images Using Deep Learning

arXiv:2404.16882v1 Announce Type: cross
Abstract: We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the localization task leverages the spatial and temporal attention mechanisms of the novel …

abstract acquired arxiv build cs.cv cs.lg data deep learning fusion image images map monitoring monitoring data part samples type