Feature Extraction of Laser Machining Data by Using Deep Multi-Task Learning

Laser machining has been widely used for materials processing, while the inherent complex physical process is rather difficult to be modeled and computed with analytical formulations. Through attending a workshop on discovering the value of laser machining data, we are profoundly motivated by the recent work by Tani et al., who proposed in situ monitoring of laser processing assisted by neural networks. In this paper, we propose an application of deep learning in extracting representative features from laser processing images with a multi-task loss that consists of cross-entropy loss and logarithmic smooth L1 loss. In the experiment, AlexNet with multi-task learning proves to be better than deeper models. This framework of deep feature extraction also has tremendous potential to solve more laser machining problems in the future.