An unsupervised online monitoring method for tool wear using a sparse auto-encoder

Tool wear, and its online monitoring, plays an important role in increasing productivity and improving product quality. We describe an unsupervised method to monitor the wear state of milling cutter by tracking an error sequence generated by reconstructing monitoring signals from a sparse auto-encoder (SAE). The monitoring signals consist of the force and vibration signals collected during the cutting process. We establish a well-structured SAE model, which can adaptively extract the characteristics of the signal and complete the training of the model without supervision of the empirical label and investigate the reconstruction performance of the model for cutting signal. On this basis, an automatic online tool wear state identification strategy is designed to monitor the milling process. The mean reconstruction error (MRE) sequence associated with tool wear is recorded in real time by reconstructing the next signal segment from the SAE model, which is trained and updated using the current signal segment. Monitoring criteria and thresholds are recommended to automate the identification of tool wear conditions based on the filtered MRE curve. Five experiments with two different milling environments are run to confirm the feasibility of tool wear monitoring using this method, and the results show that the method can be used to monitor tool wear conditions online under different milling conditions without being supervised by any empirical labels.