Convolutional neural network evaluation of additive manufacturing images, and additive manufacturing system based thereon

Abstract

An additive manufacturing system uses a trained artificial intelligence module as part of a closed-loop control structure for adjusting the initial set of build parameters in-process to improve part quality. The closed-loop control structure includes a slow control loop taking into account in-process build layer images, and may include fast control loop taking into account melt pool monitoring data. The artificial intelligence module is trained using outputs from a plurality of convolutional neural networks (CNNs) tasked with evaluating build layer images captured in-process and images of finished parts captured post-process. The post process images may include two-dimensional images of sectioned finished parts and three-dimensional CAT scan images of finished parts.