Real-time Tool Wear Monitoring Based on A Temporal Convolutional Network

As is well known, the cutting tool wear has a negative impact on machining precision. A precise tool wear monitoring method plays an important role in facilitating in-time cutting tool replacement, decreasing the risk of tool failure, and enhancing the machining precision. This work proposes an end-to-end approach for online tool wear monitoring based on deep learning. Firstly, a temporal convolutional network (TCN) is designed to extract features in time series from raw sensor data acquired during the cutting process. Secondly, a fully connected network is built to decode the extracted features into the exact value of tool wear. Finally, the approach is validated on PHM 2010 challenge dataset. Experimental studies show that the flank wear of the cutting tool can be monitored not only precisely, but also fast, indicating that the proposed approach has great prospects for application.