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<p>Automotive spiral bevel gears are typically manufactured from low-carbon alloy steel and undergo carburizing-quenching-tempering heat treatment, commonly referred to as hard-tooth surface heat treatment, to achieve a tough core and a surface with high hardness and wear resistance. However, distortion inevitably arises during the hard tooth heat treatment process, adversely affecting transmission performance and service life. To address this issue, a multi-physics coupled numerical model was established and experimentally validated to accurately predict gear deformation and microstructural evolution. The effects of key process parameters on cumulative tooth profile error and hardness were then systematically investigated using numerical simulation and experimental data. Furthermore, an integrated optimization methodology was developed, by combining a Particle Swarm Optimization-Backpropagation (PSO-BP) neural network, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the entropy weight method to synergistically control distortion and mechanical properties. The optimization strategy successfully improved gear performance while effectively mitigating distortion. Specifically, the validated parameters achieved a 14.61% reduction in the cumulative tooth profile error, decreasing it from 0.9318&#xa0;mm to 0.7957&#xa0;mm; tooth surface hardness increased by 0.74 HRC, from 58.84 HRC to 59.58 HRC; core hardness slightly raised by 0.06 HRC, from 40.15 HRC to 40.21 HRC. The strong agreement among numerical simulations, neural network predictions, and experimental results confirms the efficacy and considerable potential of the proposed methodology for enhancing quality control in the heat treatment of spiral bevel gears, offering a systematic solution for distortion control and performance enhancement in industrial gear production.</p>

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Optimization of the Hard-Tooth Surface Heat Treatment Process Parameters of Automotive Spiral Bevel Gears Based on Deformation and Performance Collaborative Control

  • 期刊论文

  • 发表日期：

    2026年2月8日

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  • Ganhua Liu,
  • Zhipeng Zhu,
  • Shaodong Chen,
  • Fuchao Yu

摘要

Automotive spiral bevel gears are typically manufactured from low-carbon alloy steel and undergo carburizing-quenching-tempering heat treatment, commonly referred to as hard-tooth surface heat treatment, to achieve a tough core and a surface with high hardness and wear resistance. However, distortion inevitably arises during the hard tooth heat treatment process, adversely affecting transmission performance and service life. To address this issue, a multi-physics coupled numerical model was established and experimentally validated to accurately predict gear deformation and microstructural evolution. The effects of key process parameters on cumulative tooth profile error and hardness were then systematically investigated using numerical simulation and experimental data. Furthermore, an integrated optimization methodology was developed, by combining a Particle Swarm Optimization-Backpropagation (PSO-BP) neural network, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the entropy weight method to synergistically control distortion and mechanical properties. The optimization strategy successfully improved gear performance while effectively mitigating distortion. Specifically, the validated parameters achieved a 14.61% reduction in the cumulative tooth profile error, decreasing it from 0.9318 mm to 0.7957 mm; tooth surface hardness increased by 0.74 HRC, from 58.84 HRC to 59.58 HRC; core hardness slightly raised by 0.06 HRC, from 40.15 HRC to 40.21 HRC. The strong agreement among numerical simulations, neural network predictions, and experimental results confirms the efficacy and considerable potential of the proposed methodology for enhancing quality control in the heat treatment of spiral bevel gears, offering a systematic solution for distortion control and performance enhancement in industrial gear production.