A deep learning approach for real time process monitoring and curling defect detection in Selective Laser Sintering by infrared thermography and convolutional neural networks
Victor Klamert, Matthias Schmid-Kietreiber, Mugdim Bublin
Abstract
Selective Laser Sintering (SLS) of polyamide powders is one of the most prevalent additive manufacturing (AM) processes. Failures induced by curling and other process irregularities affect mechanical properties and part quality. In order to improve cost- and resource-efficiency, flexibility and sustainability of SLS, a real time and in-situ quality control of the whole SLS process chain is needed. Machine learning (ML) and especially deep learning (DL) is increasingly used for SLS quality control in recent time. In this approach, we investigated applications of DL to implement an in-situ quality control of the SLS process. Especially convolutional neural networks (CNN) have been used to classify infrared thermography recordings containing artificially induced defects. Using VGG16 CNN with the thermal recordings as the input data, an average curling failure detecting accuracy of 98,54 % was achieved. These results encourage the deployment of DL for non-destructive, in-situ quality control of SLS processes.