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首页> 外文期刊>Journal of Materials Engineering and Performance >Artificial Neural Network Prediction of Fretting Wear Behavior of Structural Steel, En 24 Against Bearing Steel, En 31
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Artificial Neural Network Prediction of Fretting Wear Behavior of Structural Steel, En 24 Against Bearing Steel, En 31

机译：En 24对轴承钢，En 31的结构钢微动磨损行为的人工神经网络预测

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摘要

In this study, artificial neural network (ANN) technique is used to predict the friction and wear behavior of various surface-treated structural steel (En 24) fretted against bearing steel (En 31). A three-layer neural network with a back propagation algorithm is used to train the network. Fretting wear volume and coefficient of friction obtained at different normal loads (ranging between 2.4 and 29.4 N) for various treated samples (hardened, thermo-chemically treated, MoS2 coated) were used in the formation of training data of ANN. Results of the predictions of ANN are in good agreement with the experimental results. The degree of accuracy of predictions was 96.3 and 95.7% for fretting friction coefficient and wear, respectively.
机译：在这项研究中，使用人工神经网络（ANN）技术来预测各种表面处理的结构钢（En 24）摩擦轴承钢（En 31）的摩擦和磨损行为。具有反向传播算法的三层神经网络用于训练网络。 ANN训练数据的形成采用了不同处理（硬化，热化学处理，MoS2 涂层）在不同法向载荷（介于2.4和29.4 N之间）下获得的微动磨损量和摩擦系数。人工神经网络的预测结果与实验结果吻合良好。微动摩擦系数和磨损的预测准确度分别为96.3和95.7％。

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