Predicting the Dynamic Parameters of the Workpiece during Milling of Thin-walled Parts Based on Neural Network
Accurately predicting the time-varying dynamic parameters of workpieces during milling of thin-walled parts is the basis for selecting chatter-free cutting parameters.This paper proposes a three-layer neural network based method for predicting time-varying dynamic parameters of workpiece during milling of curved thin-walled parts.Firstly,the thin-walled part is discretized using shell elements,and the thickness of the workpiece at the discrete element nodes is taken as the input parameter,while the first three natural frequencies of the workpiece are taken as the output parame-ters to construct a three-layer neural network.Then,the results of finite element model built by the shell element are used as training samples to train the neural network model.Modal testing results show that the maximum error of the finite element model in predicting the natural frequencies of the workpiece is about 4%.Compared with the results of finite element model,the maximum prediction error of the neural network model is 0.409%.Therefore,the maximum predic-tion error of the neural network model is about 4%.At the same time,the training time of the three-layer neural network model is approximately 10 s.When the number of predicted cutting states is 150,the prediction time is only 0.002 s.The three-layer neural network model can greatly improve computational efficiency while ensuring calculation accuracy.
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