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
李泽稷 1周学良 1孙培禄2
作者信息
- 1. 湖北汽车工业学院机械工程学院,十堰 442002
- 2. 运城学院机械工程学院,运城 044000
- 折叠
摘要
为进一步提高切削加工过程刀具磨损值监测的精度,提出一种融合残差块与Swin-Transformer模型的刀具磨损监测模型.首先,采用分组卷积残差块提取信号的特征;然后,利用Swin-Transformer模型中的分块滑动窗口自注意力机制对提取的特征进行平移融合;最后,通过回归层实现刀具磨损值监测.试验结果表明,融合一层残差块与一层stage机制的Swin-Transformer模型可以有效融合刀具磨损状态监测信号的全局信息,相比其他Swin-Transformer模型,不仅模型结构简单,而且具有更高的监测精度,利用PHM2010数据集验证的MSE、MAE和R2分别达到4.471 9、1.467 5和0.995 8.
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引用本文复制引用
基金项目
国家自然科学基金资助项目(52075107)
湖北省高等学校优秀中青年科技创新团队计划项目(T2020018)
出版年
2024
国家科技期刊平台
NSTL
万方数据