Prediction of stability lobe diagrams in milling based on deep learning method

In the milling process, the stability lobe diagrams (SLD) allow the selection of appropriate milling parameters to maximize the machining efficiency. While calculating the SLD is time-consuming, this paper proposes a method for predicting the SLD based on spindle speed and depth of cut using deep learning. The prediction model constructed by this algorithm using LSTM neural network can predict the stability boundary according to different cutting depths and spindle speeds. Validation is carried out through experiments, and the results show that the proposed method has a stability prediction accuracy of 96.3% under different operating conditions. It is also verified that the proposed method can reasonably select milling parameters and improve machining efficiency.