Turning tool wear state recognition based on EMDResNeStTime and Sequence-Global-Encoding

Abstract Tool wear is inevitable in machining and directly impacts the forming quality of the workpiece. Accurate monitoring of tool wear state can effectively improve machining efficiency and reduce the adverse effects caused by tool wear. This paper proposes an intelligent recognition approach for tool wear state in machining based on vibration signals. The EMDResNeStTime (ERT) module uses empirical mode decomposition (EMD) and two-dimensional convolution to extract the main trend features of vibration signals, and the Sequence-Global-Encoding (SGE) module uses the self-attention mechanism to extract the global features of vibration signals, are designed. A new Multi-feature Parallel-time (MFPT) feature extraction backbone is constructed using these two modules. The use of this backbone effectively improves the ability of the network model to extract key features of complex trends in vibration signals. Experimental results show that the proposed approach achieves 71.65%, 74.89%, 71.36% and 72.78% in accuracy, precision, recall and F1 score, respectively. Compared with other network models, it has higher recognition accuracy and more stable performance. The ablation experiment further demonstrates the classification effectiveness of the method used in this network model.