China Mechanical Engineering ›› 2023, Vol. 34 ›› Issue (02): 185-192.DOI: 10.3969/j.issn.1004-132X.2023.02.008

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Calculation Method and Software Development of Gear Fatigue Strength under Small Samples Based on Monte-Carlo Simulation

LI Yang;LIU Huaiju;WEI Peitang;MAO Tianyu;CHEN Difa   

  1. State Key Laboratory of Mechanical Transmissions,Chongqing University,Chongqing,400044
  • Online:2023-01-25 Published:2023-02-16

基于Monte-Carlo模拟的小样本下齿轮疲劳极限计算方法及软件开发

李扬;刘怀举;魏沛堂;毛天雨;陈地发   

  1. 重庆大学机械传动国家重点实验室,重庆,400044
  • 通讯作者: 刘怀举(通信作者),男,1986年生,教授、博士研究生导师。研究方向为机械传动智能设计与抗疲劳制造。发表论文100余篇。E-mail:huaijuliu@cqu.edu.cn。
  • 作者简介:李扬,男,1999年生,硕士研究生。研究方向为机械传动智能设计与软件开发。E-mail:202107131116t@cqu.edu.cn。
  • 基金资助:
    国家自然科学基金(52175041);国家科技重大专项(2019-Ⅶ-0017-0158)

Abstract: The Dixon-Mood(D-M)method used in gear fatigue strength evaluation needed a certain amount samples of tests, and the results might contain deviations. Thus an effective estimation method(the CQUboot method)of gear fatigue strength was proposed based on the Monte-Carlo simulation. D-M method and CQUboot method were verified and compared by a large number of bending fatigue strength tests. Compared with the results of 20~22 samples recommended in GB/T 14230—2021 Test Method of Tooth Bending Strength for Gear Load Capacity, while the sample number reduces to 12, the maximum errors of the D-M method are as 15.76%~24.99%, while that of the CQUboot method is as 8.02%~12.98%. With 12.66% as the maximum allowable error of fatigue strength estimation, the D-M method at least needs 10~18 samples, while the CQUboot method only needs 8 samples. 

Key words: gear fatigue strength, Dixon-Mood method, Monte-Carlo simulation, test verification, software development

摘要: 齿轮疲劳极限评估Dixon-Mood(D-M)法的试验样本需求量较大,标准差估计值存在较大偏差,因此基于Monte-Carlo模拟提出小样本的齿轮疲劳极限分析方法(CQUboot),并结合大量弯曲疲劳极限试验对D-M法和CQUboot法进行了对比分析。与GB/T 14230—2021《齿轮弯曲疲劳强度试验方法》推荐的20~22个样本计算结果相比,样本量降至12时,D-M法的最大误差为15.76%~24.99%,CQUboot法为8.02%~12.98%;以12.66%为疲劳极限预估允许的最大误差时,D-M法至少需要10~18个样本点,CQUboot法只需8个样本点。

关键词: 齿轮疲劳极限, Dixon-Mood法, Monte-Carlo模拟, 试验验证, 软件开发

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