现代制造工程Issue(8)：126-135,10.DOI:10.16731/j.cnki.1671-3133.2024.08.016
融合残差块与Swin-Transformer机制的刀具磨损监测方法
Tool wear monitoring methods incorporating residual block and Swin-Transformer mechanisms
摘要
Abstract
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.关键词
刀具/磨损监测/残差卷积神经网络/Swin-Transformer模型Key words
cutting tool/wear monitoring/residual convolutional neural networks/Swin-Transformer model分类
矿业与冶金引用本文复制引用
李泽稷,周学良,孙培禄..融合残差块与Swin-Transformer机制的刀具磨损监测方法[J].现代制造工程,2024,(8):126-135,10.基金项目
国家自然科学基金资助项目(52075107) （52075107）
湖北省高等学校优秀中青年科技创新团队计划项目(T2020018) （T2020018）
