Finite Element Modeling Of Direct Metal Laser Solidification Process: Sensor Data Replication And Use In Defect Detection And Data Reduction Via Machine Learning

In the additive manufacturing (AM) industry, the direct metal laser solidification (DMLS) process has received significant research interest because of its ultra-high precision and geometry variability. However, many possible process errors can happen during the printing process and in-situ quality control is quite challenging because of the lack of appropriate monitoring and analysis capabilities. Thus, in this work, a part-scale finite element method (FEM) model is developed to investigate the heat transfer behavior of the DMLS process and to extract experimentally relevant thermal features. Specifically, a microscopic and a mesoscale sub-model are initially developed to describe powder properties and the laser behaviors, respectively, and their outputs are directly incorporated in the part-scale FEM model. The FEM model-generated data are then processed to replicate the long-wave infrared (LWIR) camera sensor outputs. Finally, both the experimental and FEM model generated images are used to train machine learning algorithms for in-situ defect detection applications. In addition, we are endeavoring to look into a convolutional neural network utilizing such realistic thermal features from LWIR images with and without a variety of simulated data augmentation to examine its contribution to the CNN training. The thermal feature extraction and the machine learning algorithm are then utilized for the data reduction purpose. Also, a transmission strategy is demonstrated to filter out a significant amount of redundant data while maintaining high model prediction quality. (c) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.