Online Tool Wear Monitoring Based on Feature Fusion and Domain Adaptation
Machine tool condition monitoring is of great significance for machine tool health management and workpiece processing quality assurance.In view of the problems of existing tool wear prediction models such as long training time,slow convergence speed and weak generalization ability,this paper proposes a distributed one-dimensional convolutional neural network to predict tool wear.The residual connection and channel attention modules are sequentially stacked as the feature extraction module,and cross-validation was used to select the appropriate number of network layers.Since the feature information extracted by dif-ferent sensors may be redundant,a weight difference strategy was used to improve the effectiveness and comprehensiveness of feature extraction.In addition,considering that the distribution of the training set and the test set may be different,which affects the generalization performance of the model,a domain adapta-tion method is introduced to improve the performance of the model in unknown data sets.In order to verify the effect of the model,experiments were carried out using the PHM 2010 milling cutter wear data set.The experimental results show that the average RMSE and average MAE of the model on the three tools C1,C4,and C6 are 6.97 and 6.29,respectively,which is more than 12%improved compared with TCN,TDConv-LSTM and other models.
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