| 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 |
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| 引用本文： | 李扬,刘怀举,魏沛堂,毛天雨,陈地发.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[J].中国机械工程,2023,34(2):185-192. |
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| 作者姓名： | 李扬 刘怀举 魏沛堂 毛天雨 陈地发 |
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| 作者单位： | 重庆大学机械传动国家重点实验室，重庆，400044 |
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| 基金项目： | 国家自然科学基金（52175041）；国家科技重大专项（2019-Ⅶ-0017-0158） |
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| 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。
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| 关 键 词： | 齿轮疲劳极限 Dixon-Mood法 Monte-Carlo模拟 试验验证 软件开发 |
Calculation Method and Software Development of Gear Fatigue Strength under Small Samples Based on Monte-Carlo Simulation |
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| LI Yang,LIU Huaiju,WEI Peitang,MAO Tianyu,CHEN Difa.Calculation Method and Software Development of Gear Fatigue Strength under Small Samples Based on Monte-Carlo Simulation[J].China Mechanical Engineering,2023,34(2):185-192. |
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| Authors: | LI Yang LIU Huaiju WEI Peitang MAO Tianyu CHEN Difa |
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| Affiliation: | State Key Laboratory of Mechanical Transmissions，Chongqing University，Chongqing，400044 |
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| 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. |
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| Keywords: | gear fatigue strength Dixon-Mood method Monte-Carlo simulation test verification software development |
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