In-situ capture of melt pool signature in selective laser melting using U-Net-based convolutional neural network

Selective laser melting (SLM) is an additive manufacturing technology that has an extensively applied foreground and practical value in many fields. Despite its powerful manufacturing ability, defects are prone to occur and therefore a more reliable and repeatable manufacture process is in high demand. During the SLM process, the melt pool signature is the key to understanding the dynamic process status, with which it is possible to predict process failure and give guidance to real-time feedback control. In this paper, a novel method to capture melt pool signature using a U-Net-based convolutional neural network is described. A lightweight architecture was used to reduce the inference time, and an improved loss function with penalty maps was applied to better remove interferences. The model performance was evaluated by comparing both the processing time and accuracy with two conventional image segmentation algorithms, including the threshold segmentation method and the active contour method. Mean intersection over union (MIoU) was chosen as the segmentation metric. Unlike traditional algorithms, U-Net successfully eliminated the interferences, and reached the highest MIoU (0.9806) at a rela-tively low computational cost of 37 ms on average. The collected information from the melt pool area in various scenarios was analyzed, and its potential to indicate the problem of melt pool overheating was investigated.

Automated summary

Selective laser melting (SLM) is an additive manufacturing technology with extensive applications, but defects are prone to occur, requiring a more reliable and repeatable manufacturing process. A novel method using a U-Net-based convolutional neural network to capture melt pool signature was described, achieving better results with reduced inference time.