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<p>In the harmonic drive (HD) system, the wave generator (WG) plays a crucial role in its function in the aspect of lifespan, deformation in flexspline (FS), and transmission accuracy. However, conventional elliptical WG profiles remain dominant, leading to restricted functionality. This paper presents a machine learning (ML) based optimization framework for WG profiles, using support function. Three performance indicators—FS stress, engagement ratio, and WG curvature—are evaluated. Finite element model (FEM) is built to simulate stress distribution and deformation patterns. Surrogate models using different ML methods trained on 331 datasets overcome computational bottlenecks, with the XGBoost network showing the highest accuracy. Genetic algorithm optimization reduces stress by 21.70 %, boosts engagement by 12.42 %, and lowers curvature by 62.49 % versus traditional elliptical profiles. Tooth profile modification mitigates interference. The scalable framework benefits precision transmission systems.</p>

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Machine learning based design optimization method for wave generators of harmonic drives considering multiple performance indicators

  • 期刊论文

  • 发表日期：

    2026年1月13日

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  • Jiawen Yang,
  • Hongyu Wu,
  • Xiaobiao Mu,
  • Guoqiang Yang,
  • Shaoze Yan

摘要

In the harmonic drive (HD) system, the wave generator (WG) plays a crucial role in its function in the aspect of lifespan, deformation in flexspline (FS), and transmission accuracy. However, conventional elliptical WG profiles remain dominant, leading to restricted functionality. This paper presents a machine learning (ML) based optimization framework for WG profiles, using support function. Three performance indicators—FS stress, engagement ratio, and WG curvature—are evaluated. Finite element model (FEM) is built to simulate stress distribution and deformation patterns. Surrogate models using different ML methods trained on 331 datasets overcome computational bottlenecks, with the XGBoost network showing the highest accuracy. Genetic algorithm optimization reduces stress by 21.70 %, boosts engagement by 12.42 %, and lowers curvature by 62.49 % versus traditional elliptical profiles. Tooth profile modification mitigates interference. The scalable framework benefits precision transmission systems.