Tool wear monitoring methods incorporating residual block and Swin-Transformer mechanisms
To further improve the accuracy of tool wear value monitoring in the cutting machining process,a tool wear monitoring model that integrated the residual block and Swin-Transformer model was proposed.Firstly,the grouped convolutional residual block was used to extract the features of the signal.Then,the chunked sliding window self-attention mechanism in the Swin-Trans-former model was used to translate the extracted features.Finally,the tool wear value prediction was realized through the regression layer.The experimental results show that the Swin-Transformer model fusing a layer of residual blocks with a layer of stage mecha-nism can effectively fuse the global information of tool wear state monitoring signals,which not only has a simple model structure but also has a higher monitoring accuracy compared with other Swin-Transformer models,and the MSE,MAE,and R2 verified by utilizing the PHM2010 dataset reached 4.471 9,1.467 5,and 0.995 8,respectively.
cutting toolwear monitoringresidual convolutional neural networksSwin-Transformer model
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