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<p>High-speed dry hobbing is a very promising green Machining process for gears, but the adaptability of high-speed dry gear hobbing process in Manufacturing high-performance gears is severely limited due to the highly nonlinear relationship between the normal deviation of the shaped tooth flanks and the tool topology and process parameters. In this paper, the multivariate nonlinear relationship between the number of hob teeth, the number of cutting edges, the pitch radius, the feed and the normal deviation of the shaped tooth surface is revealed based on the polynomial-lasso regression model from the principle of gear meshing. On this basis, the Bayesian optimization algorithm was applied to optimize the parameter configurations with the minimum normal deviation of formed tooth surface as the fitness function. The optimal parameter configuration in this working condition is determined as the hob has 3 teeth and 19 flutes, with a pitch radius of roughly 34.855&#xa0;mm and a feed rate of 1.307&#xa0;mm/r. Finally, according to the optimized results, the hobbing experiments are carried out on the high-speed dry gear hobbing machine YE3115CNC, and the results show that the Maximal absolute error between the experimental results and numerical calculations is 2.0945&#xa0;μm. The results of the research in this paper can be used for the reduction of the number of test machining times and the reduction of the design cost.</p>

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Optimal Machining Parameters Selection for High Quality Tooth Surface by High-speed Dry Gear Hobbing

  • 期刊论文

  • 发表日期：

    2025年9月12日

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  • Yongpeng Chen,
  • Lin Li,
  • Xin Liu,
  • Guiyou Yang,
  • Bing Cao

摘要

High-speed dry hobbing is a very promising green Machining process for gears, but the adaptability of high-speed dry gear hobbing process in Manufacturing high-performance gears is severely limited due to the highly nonlinear relationship between the normal deviation of the shaped tooth flanks and the tool topology and process parameters. In this paper, the multivariate nonlinear relationship between the number of hob teeth, the number of cutting edges, the pitch radius, the feed and the normal deviation of the shaped tooth surface is revealed based on the polynomial-lasso regression model from the principle of gear meshing. On this basis, the Bayesian optimization algorithm was applied to optimize the parameter configurations with the minimum normal deviation of formed tooth surface as the fitness function. The optimal parameter configuration in this working condition is determined as the hob has 3 teeth and 19 flutes, with a pitch radius of roughly 34.855 mm and a feed rate of 1.307 mm/r. Finally, according to the optimized results, the hobbing experiments are carried out on the high-speed dry gear hobbing machine YE3115CNC, and the results show that the Maximal absolute error between the experimental results and numerical calculations is 2.0945 μm. The results of the research in this paper can be used for the reduction of the number of test machining times and the reduction of the design cost.