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<p>In the process of material processing, the wear state of the tool is the key factor affecting the processing quality. However, the existing wear state judgment methods rely on manual experience and have low accuracy, resulting in a decline in the processing quality of the workpiece. Aiming at this problem, this paper proposes a tool wear trend prediction method based on deep residual shrinkage network and auxiliary particle filter. This method takes the vibration signal of the tool base as the input, and the surface roughness of the workpiece as the wear label. Finally, the real-time prediction value of the tool wear trend and the future multi-step prediction value can be obtained. In the comparative verification experiment, the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:{R}^{2}\)</EquationSource> </InlineEquation> values of real-time prediction results and future multi-step prediction results are 0.9762 and 0.72, respectively. Both are superior to the results of other classical models. The results show that compared with the traditional method, this method can predict the wear trend of the tool more accurately in a complex machining environment. This method provides a new solution for intelligent operation and maintenance in product processing, and has high application value.</p>

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Advanced predictive technology: a hybrid DRSN-APF framework for real-time and multi-step tool wear trend prediction

  • 期刊论文

  • 发表日期：

    2026年7月31日

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  • Yangzhou Liu,
  • Ping Yan,
  • Liguo Zhang,
  • Sichen Li,
  • Hongjin Zhai,
  • Chunjie Ma

摘要

In the process of material processing, the wear state of the tool is the key factor affecting the processing quality. However, the existing wear state judgment methods rely on manual experience and have low accuracy, resulting in a decline in the processing quality of the workpiece. Aiming at this problem, this paper proposes a tool wear trend prediction method based on deep residual shrinkage network and auxiliary particle filter. This method takes the vibration signal of the tool base as the input, and the surface roughness of the workpiece as the wear label. Finally, the real-time prediction value of the tool wear trend and the future multi-step prediction value can be obtained. In the comparative verification experiment, the \(\:{R}^{2}\) values of real-time prediction results and future multi-step prediction results are 0.9762 and 0.72, respectively. Both are superior to the results of other classical models. The results show that compared with the traditional method, this method can predict the wear trend of the tool more accurately in a complex machining environment. This method provides a new solution for intelligent operation and maintenance in product processing, and has high application value.