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The cutting tool is a key component of the milling machine, which is a key factor in ensuring processing quality, improving production efficiency, and reducing production energy consumption and time costs. It is also the most easily damaged and severely wasted component. When the milling cutter reaches a certain level, it is necessary to replace the cutter timely to avoid affecting processing quality. Therefore, how to predict and analyze the wear degree of milling cutters has become an urgent problem to be solved for intelligent milling processing. This paper monitors the status of the milling cutter by installing three kinds of sensors on the workpiece and uses a CNN prediction model to predict wear degree for the milling cutter. The experiment results have shown that our prediction model can improve the efficiency of tool usage while ensuring product processing quality, thus truly achieving the goal of reducing costs and increasing efficiency.

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Prediction and Analysis of Milling Cutter Wear Degree Based on CNN Model

  • 会议论文

  • 发表日期：

    2025年

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  • Xiaogang Wang,
  • Lin Zhu,
  • Chen Yi-Chang

摘要

The cutting tool is a key component of the milling machine, which is a key factor in ensuring processing quality, improving production efficiency, and reducing production energy consumption and time costs. It is also the most easily damaged and severely wasted component. When the milling cutter reaches a certain level, it is necessary to replace the cutter timely to avoid affecting processing quality. Therefore, how to predict and analyze the wear degree of milling cutters has become an urgent problem to be solved for intelligent milling processing. This paper monitors the status of the milling cutter by installing three kinds of sensors on the workpiece and uses a CNN prediction model to predict wear degree for the milling cutter. The experiment results have shown that our prediction model can improve the efficiency of tool usage while ensuring product processing quality, thus truly achieving the goal of reducing costs and increasing efficiency.