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<p>Tooth surface deviation is a key indicator of the geometric accuracy of spiral bevel gears, significantly affecting transmission performance and service life. However, conventional inspection methods cannot provide online evaluation. This paper proposes an error estimation method for gear grinding based on spindle vibration signals, establishing a mapping model between spindle displacement and tooth surface deviation. Displacements were obtained from acceleration data using frequency-domain integration, and experimental measurements were incorporated into the error model. Results from an orthogonal experiment revealed that axial vibration dominates error accumulation, with generating speed and grinding depth exerting the greatest influence on surface deviation, while wheel speed plays a secondary role. The proposed method enables effective online prediction of machining errors and provides a theoretical basis for real-time monitoring, error compensation, and process optimization in spiral bevel gear grinding.</p>

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Error modeling and tooth surface deviation prediction in spiral bevel gear grinding based on spindle vibration signals

  • 期刊论文

  • 发表日期：

    2026年3月13日

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  • Rui Xue,
  • Jiang Han,
  • Lian Xia,
  • Xiaoqing Tian,
  • Guyu Li,
  • Xin Du,
  • Nan Liu,
  • Kai Wu,
  • Zhiyong Wang

摘要

Tooth surface deviation is a key indicator of the geometric accuracy of spiral bevel gears, significantly affecting transmission performance and service life. However, conventional inspection methods cannot provide online evaluation. This paper proposes an error estimation method for gear grinding based on spindle vibration signals, establishing a mapping model between spindle displacement and tooth surface deviation. Displacements were obtained from acceleration data using frequency-domain integration, and experimental measurements were incorporated into the error model. Results from an orthogonal experiment revealed that axial vibration dominates error accumulation, with generating speed and grinding depth exerting the greatest influence on surface deviation, while wheel speed plays a secondary role. The proposed method enables effective online prediction of machining errors and provides a theoretical basis for real-time monitoring, error compensation, and process optimization in spiral bevel gear grinding.