Milling Tool Wear Monitoring by Using Gated Recurrent Unit Neural Network and Multi-feature Fusion
To realize the tool wear condition monitoring in the production of a vehicle engine's cylinder head and to enhance the computational efficiency and recognition accuracy of tool wear monitoring,a tool condition monitoring method based on the gated recurrent unit neural network and the multi-feature fusion method is proposed for identifying the width of milling tool flank wear.The effectiveness of the proposed method is verified with the milling force signal data,and the effects of different hyper-parameter settings on the model recognition accuracy is analyzed.The optimal hyper-parameters are given;the accurate recognition of milling tool wear is realized.
tool wearmilling force signalcondition monitoringGRU neural network