Evaluation of Deep Learning for Semantic Image Segmentation in Tool Condition Monitoring

Abstract

Tool wear is one of the main factors of manufacturing costs in subtractive manufacturing processes. To control manufacturing processes while taking the tool wear into account, a variety of tool condition monitoring systems have been investigated. In this paper, we present a new approach to support the manual analysis of tool wear images by the means of semantic image segmentation. We utilize deep learning for image evaluation through semantic classification of different defect regions. In this study, a small-sized dataset of 100 cutting tool inserts at different tool conditions, exhibiting various wear defects, is acquired and masked by a process expert. A sliding window approach is used to extract small size feature maps from the raw images, with the class of the center pixel as the label. The relationship between the features and the label is trained using a convolutional neural network. Our investigation shows that this network can predict the wear defect class of each pixel with an accuracy of over 91%. Compared to other approaches, the proposed solution can differentiate between various defect types, for instance, flank wear, groove formation and build-up-edge. From the resulting segmented image, different wear metrics are computed, such as the maximum flank wear width or the occurrence and size of other wear defects. This information is fed back to the machine operator to support the decision process of whether to continue machining, adapt the cutting conditions or exchange the insert.