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With the increasing demands of high-end manufacturing industries for processing quality and efficiency, real-time and accurate monitoring of tool wear during milling has become a critical issue in the field of mechanical processing. To address this, this paper proposes an online monitoring technology for tool wear based on the Integrated Multi-Scale Temporal Convolutional-Attention (IMSTCA) model. By synchronizing data from multi-source sensors, such as cutting force, vibration, and acoustic emission, the model integrates the advantages of multi-scale feature fusion and deep temporal feature extraction to monitor and warn of tool wear in real time. IMSTCA consists of two key modules: the Multi-Scale Selective Kernel Convolutional Network (MS-SKCNN) and the Residual Temporal Convolutional Network with Integrated Attention Module (RTCN-IAM). The former processes high-dimensional multi-channel signals through multi-scale convolutions, using a selective kernel strategy to adaptively choose the optimal branch for various wear characteristics. The latter, using deep stacked temporal convolution and an integrated attention mechanism, automatically focuses on key patterns from both the channel and time dimensions, capturing long-term and short-term dependencies in the wear evolution. Through ablation experiments on the PHM dataset, the IMSTCA model demonstrates good monitoring accuracy and generalization ability, outperforming other models in terms of RMSE, MAE, MAPE and R2.

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Milling Tool Wear Monitoring Based on IMSTCA Model

  • 会议论文

  • 发表日期：

    2026年

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  • Hanqi Yan,
  • Xiaojian Liu,
  • Yang Wang,
  • Shuyou Zhang,
  • Jianzhong Fu,
  • Yiming Zhang

摘要

With the increasing demands of high-end manufacturing industries for processing quality and efficiency, real-time and accurate monitoring of tool wear during milling has become a critical issue in the field of mechanical processing. To address this, this paper proposes an online monitoring technology for tool wear based on the Integrated Multi-Scale Temporal Convolutional-Attention (IMSTCA) model. By synchronizing data from multi-source sensors, such as cutting force, vibration, and acoustic emission, the model integrates the advantages of multi-scale feature fusion and deep temporal feature extraction to monitor and warn of tool wear in real time. IMSTCA consists of two key modules: the Multi-Scale Selective Kernel Convolutional Network (MS-SKCNN) and the Residual Temporal Convolutional Network with Integrated Attention Module (RTCN-IAM). The former processes high-dimensional multi-channel signals through multi-scale convolutions, using a selective kernel strategy to adaptively choose the optimal branch for various wear characteristics. The latter, using deep stacked temporal convolution and an integrated attention mechanism, automatically focuses on key patterns from both the channel and time dimensions, capturing long-term and short-term dependencies in the wear evolution. Through ablation experiments on the PHM dataset, the IMSTCA model demonstrates good monitoring accuracy and generalization ability, outperforming other models in terms of RMSE, MAE, MAPE and R2.