Abstract：A tool wear monitoring model based on one-dimensional residual network with threshold module and a tool wear prediction model based on bidirectional long short-term memory network are designed. The sensor signal is input into the monitoring model after wavelet decomposition, the threshold module automatically selects the threshold to reduce the noise of the signal and the residual module extracts the signal characteristics, then outputs the tool wear monitoring value, and inputs it into the prediction model to obtain the tool wear prediction value. The experimental results show that the monitoring accuracy of this monitoring model is improved by 0.327% and 1.697% respectively compared with the one-dimensional residual network model without threshold module and convolution neural network model; the prediction effect of the prediction model is good, and the absolute error is only 0.024.
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2022, Vol. 43 
