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<p>The cutting force coefficients are important parameters in the milling process, which not only affects the milling stability but also directly reflects the tool wear state. This paper proposed a method for online identification of cutting force coefficients and prediction of tool wear state based on the current and voltage of the feed drive. The method is based on the instantaneous electromagnetic torque theory and utilizes a neural network to establish a complex mapping relationship between current, voltage, and instantaneous milling force, achieving high-precision online prediction of the milling force waveform during the milling process. Then, a method for online identification of cutting force coefficients and prediction of tool wear state is proposed by integrating the predicted milling force with machining information. Through the experiments on different cutting conditions and materials, the results show that the proposed method can identify the cutting force coefficients under different machining conditions by fully training the prediction model and predicting the wear state of milling cutters by online monitoring the variation trend of cutting force coefficients along with the wear process developing, and accurately determine the key points of milling cutters replacement.</p>

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Online tool wear prediction based on cutting force coefficients identification using neural network

  • 期刊论文

  • 发表日期：

    2025年2月8日

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  • Guicheng Wang,
  • Min Wang,
  • Peng Gao,
  • Bin Yang

摘要

The cutting force coefficients are important parameters in the milling process, which not only affects the milling stability but also directly reflects the tool wear state. This paper proposed a method for online identification of cutting force coefficients and prediction of tool wear state based on the current and voltage of the feed drive. The method is based on the instantaneous electromagnetic torque theory and utilizes a neural network to establish a complex mapping relationship between current, voltage, and instantaneous milling force, achieving high-precision online prediction of the milling force waveform during the milling process. Then, a method for online identification of cutting force coefficients and prediction of tool wear state is proposed by integrating the predicted milling force with machining information. Through the experiments on different cutting conditions and materials, the results show that the proposed method can identify the cutting force coefficients under different machining conditions by fully training the prediction model and predicting the wear state of milling cutters by online monitoring the variation trend of cutting force coefficients along with the wear process developing, and accurately determine the key points of milling cutters replacement.