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首页> 外文期刊>Journal of Applied Polymer Science >Tribological behavior prediction of friction materials for ultrasonic motors using Monte Carlo-based artificial neural network
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Tribological behavior prediction of friction materials for ultrasonic motors using Monte Carlo-based artificial neural network

机译：基于蒙特卡罗的人工神经网络的超声波电动机摩擦材料的摩擦学行为预测

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摘要

In this article, the relationship of complexity, diversity, and uncertainty between components and tribological properties of friction materials based on a Monte Carlo-based artificial neural network (MC-ANN) model was predicted precisely. Meanwhile, the grey relational analysis was applied to figure out weight of factors, optimize formulation design, and calculate nonlinear dependency of ingredients. The accuracy of model was studied by comparing experimental and simulated values on the basis of statistical methods (root-mean-squared error). It was found that the model exhibited an excellent performance in predicting and fitting effect. Moreover, comprehensive analysis of weight indicated that nano-SiO2 and mica exerted a significant role in improving the friction stability and wear resistance. According to different contents of each ingredient, the corresponding friction coefficient and specific wear rate could be obtained by virtue of a well-trained MC-ANN model without experiments, which saved a lot of time and money. It can be expected that the results of this work will extend the current research and pave a route for further in-depth studies of friction materials. (c) 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 47157.
机译：在本文中，精确地预测了基于蒙特卡罗基人工神经网络（MC-ANN）模型的复杂性，多样性和摩擦材料之间的复杂性，多样性和不确定性的关系。同时，灰色关系分析应用于计算因子的重量，优化配方设计，并计算成分的非线性依赖性。通过比较统计方法（根均方误差）的实验和模拟值进行研究的模型的准确性。发现该模型在预测和拟合效果方面表现出优异的性能。此外，重量综合分析表明，纳米SiO2和云母在提高摩擦稳定性和耐磨性方面发挥了重要作用。根据每种成分的不同内容物，通过没有实验的训练良好的MC-Ann模型，可以获得相应的摩擦系数和特定磨损率，其挽救了大量的时间和金钱。可以预期，这项工作的结果将延长目前的研究和铺设了一种进一步深入研究摩擦材料的路线。 （c）2018 Wiley期刊，Inc.J.Phill。聚合物。 SCI。 2019,136,47157。

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