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<p>Accurate prediction of tool wear is essential for ensuring machining stability, product quality, and process efficiency in high-speed milling. However, traditional time-based wear models require extensive full-life testing under each parameter set, making them impractical for modern multi-condition machining. This study develops an enhanced and generalizable tool-wear modeling framework based on Differential Wear Signature (DWS) analysis, which formulates the wear rate as a function of cutting parameters and instantaneous wear state. By leveraging the tool runout effect, the model captures multiple effective feed rates within a single experiment, thereby improving data efficiency and reducing experimental effort. The enhanced DWS framework incorporates a broader range of cutting parameters, integrates Remaining Useful Life (RUL) estimation, and is benchmarked against conventional offline models including empirical regression, GPR, and ANN-based approaches. Experimental validation confirms that the proposed model accurately reconstructs wear trajectories and predicts RUL under both single- and multi-step cutting scenarios, achieving a root-mean-square error of 0.008&#xa0;mm, compared to 0.028&#xa0;mm in the prior DWS model. The results demonstrate that the proposed framework provides a robust, data-efficient, and transferable solution for offline tool-life prediction and process planning in CNC milling.</p>

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Robust tool wear modeling based on differential wear signature analysis

  • 期刊论文

  • 发表日期：

    2025年11月15日

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  • Shang-Yu Lin,
  • Po-Han Chen,
  • Pang-Hsiang Hung,
  • Yuh‑Chung Hu,
  • Pei-Zen Chang,
  • Wei-Chang Li

摘要

Accurate prediction of tool wear is essential for ensuring machining stability, product quality, and process efficiency in high-speed milling. However, traditional time-based wear models require extensive full-life testing under each parameter set, making them impractical for modern multi-condition machining. This study develops an enhanced and generalizable tool-wear modeling framework based on Differential Wear Signature (DWS) analysis, which formulates the wear rate as a function of cutting parameters and instantaneous wear state. By leveraging the tool runout effect, the model captures multiple effective feed rates within a single experiment, thereby improving data efficiency and reducing experimental effort. The enhanced DWS framework incorporates a broader range of cutting parameters, integrates Remaining Useful Life (RUL) estimation, and is benchmarked against conventional offline models including empirical regression, GPR, and ANN-based approaches. Experimental validation confirms that the proposed model accurately reconstructs wear trajectories and predicts RUL under both single- and multi-step cutting scenarios, achieving a root-mean-square error of 0.008 mm, compared to 0.028 mm in the prior DWS model. The results demonstrate that the proposed framework provides a robust, data-efficient, and transferable solution for offline tool-life prediction and process planning in CNC milling.