4.7 Article

Robust geometric parameter optimization of a crossed beveloid gear pair with approximate line contact

期刊

MECHANISM AND MACHINE THEORY
卷 168, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.mechmachtheory.2021.104596

关键词

Beveloid gear; Approximate line contact; Robust optimization; Contact pressure; Contact ratio

资金

  1. National Key Research and Development Plan [2019YFB1703700]
  2. National Natural Science Foundation, China [51875066]
  3. China Scholarship Council [202006050181]

向作者/读者索取更多资源

An enhanced design method for a beveloid gear with approximate line contact is proposed, along with a robust optimization model for geometric parameters of crossed beveloid gear pairs. The model considers evaluation items such as maximum contact pressure, contact trace offset distance, and contact ratio as objective functions in a robust design optimization problem. The model utilizes a fast elitist nondominated sorting genetic algorithm (NSGA-II) to obtain the Pareto front of the macroscopic geometric parameters.
For a crossed beveloid gear pair with approximate line contact, the macroscopic geometric parameter is a dominant factor that affects meshing characteristics. However, geometric parameter design and reliability under misalignment are of great difficulty. To deal with these issues, an enhanced design method for a beveloid gear with approximate line contact is firstly proposed. Based on this, a robust optimization model of geometric parameters for crossed beveloid gear pairs with approximate line contact is developed. Three evaluation items are considered, namely the maximum contact pressure, contact trace offset distance and contact ratio. To reduce the calculation time, the influence coefficient method is applied to calculate the contact pressure. Considering the characteristics of installation deviation uncertainty and distribution, the mean maximum contact pressure, contact trace offset distance and ratio are considered as objective functions, formulated as a robust design optimization problem. Finally, robust optimization of a crossed beveloid gear pair based on a fast elitist nondominated sorting genetic algorithm (NSGA-II) is applied to solve the model to obtain the Pareto front of the macroscopic geometric parameters. A numerical example is provided to demonstrate the effectiveness of the proposed method.

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