Abstract

In this study, rough elliptic bore journal bearing performance is predicted using an artificial neural network (ANN) technique. The effects of non-circularity and roughness are quantified to elliptic and isotropic in macro and micro scale, respectively. The numerically estimated performance parameters like load, friction, and flow-in at different eccentricities [0.3 (low), 0.5 (medium), and 0.8 (high)], non-circularities [0.5 (low), 1.0 (medium), and 2.0 (high)], and roughness factors [0.1 (low), 0.2 (medium), 0.3 (medium), and 0.4 (high)] are used to train and build the ANN model. The training continued until the maximum mean square error is achieved, and the best-fitting plot is generated. With a confidence level of 99.75% or an R-value of 0.99757, the results predicted are found to be satisfactory.

中文翻译：

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人工神经网络在粗椭圆孔径向轴承润滑性能评价中的应用

摘要

在这项研究中，使用人工神经网络 (ANN) 技术预测粗糙的椭圆孔轴颈轴承性能。非圆度和粗糙度的影响分别在宏观和微观尺度上量化为椭圆和各向同性。在不同偏心率 [0.3（低）、0.5（中）和 0.8（高）]、非圆度 [0.5（低）、1.0（中）和2.0（高）]和粗糙度因子[0.1（低）、0.2（中）、0.3（中）和0.4（高）]用于训练和构建ANN模型。训练一直持续到达到最大均方误差，并生成最佳拟合图。置信水平为 99.75% 或R值为 0.99757，预测结果令人满意。