Abstract Polymer gears have shown potential in power transmission by their comprehensive mechanical properties. One of the significant concerns with expanding their applications is the deficiency of reliability evaluation methods considering small data set circumstances. This work conducts a fair number of polyoxymethylene (POM) gear durability tests with adjustable loading and lubrication conditions via a gear durability test rig. A novel machine learning-based reliability model is developed to evaluate contact fatigue reliability for the POM gears with such a data set. Results reveal that the model predicts reasonable POM gear contact fatigue curves of reliability–stress–number of cycles with 2.0% relative error and 18.8% reduction of test specimens compared with the large sample data case. In contrast to grease lubrication, the oil-lubricated POM gear contact fatigue strength improves by 10.4% from 52.1 to 57.6 MPa.

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基于机器学习的小数据集聚合物齿轮接触疲劳可靠性评估

摘要 聚合物齿轮以其综合力学性能在动力传动中显示出潜力。扩展其应用的一个重要问题是考虑小数据集情况的可靠性评估方法的不足。这项工作通过齿轮耐久性试验台进行了大量聚甲醛 (POM) 齿轮耐久性试验，载荷和润滑条件可调节。开发了一种新的基于机器学习的可靠性模型，以使用此类数据集评估 POM 齿轮的接触疲劳可靠性。结果表明，与大样本数据情况相比，该模型预测了合理的 POM 齿轮接触疲劳曲线的可靠性-应力-循环次数，相对误差为 2.0%，试样减少了 18.8%。与脂润滑相比，