Data Treatment of In Situ Monitoring Systems in Selective Laser Melting Machines

Quality assurance of the final build part in laser-powder bed fusion (L-PBF) is greatly influenced by the various process steps such as powder handling, powder bed spreading, and laser-material interaction. Each process step is interlinked to each other and can affect the overall behavior of the succeeding steps. Therefore, it is vital to monitor each step individually, post-process, and establish a link among the data to develop an approach to quantify the defects via inline monitoring. This study focuses on using pre- and post-exposure powder bed image data and in situ melt pool monitoring (MPM) data to monitor the build's overall quality. Two convolutional neural networks have been trained to treat the pre and post-exposure images with a trained accuracy of 93.16% and 96.20%, respectively. The supervised machine-learning algorithm called "support vector machine" is used to classify and post-process the photodiodes data obtained from the MPM. A case study on "benchmark part" is presented to check the proposed algorithms' overall working and detect abnormalities at three different process steps (pre and post-exposure, MPM) individually. This study shows the potential of machine learning approaches to improve the overall reliability of the (L-PBF) process by inter-linking the different process steps.