Semi-Supervised Convolutional Neural Networks for In-Situ Video Monitoring of Selective Laser Melting

Selective Laser Melting (SLM) is a metal additive manufacturing technique. The lack of SLM process repeatability is a barrier for industrial progression. SLM product quality is hard to control, even when using fixed system settings. Thus SLM could benefit from a monitoring system that provides quality assessments in real-time. Since there is no publicly available SLM dataset, we ran experiments to collect over one thousand videos, measured the physical output via height map images, and applied a proposed image processing algorithm to them to produce a dataset for semi-supervised learning. Then we trained convolutional neural networks (CNNs) to recognize desired quality metrics from videos. Experimental results demonstrate our the effectiveness of our proposed monitoring approach and also show that the semi-supervised model can mitigate the time and expense of labeling an entire SLM dataset.