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Prediction of contact lengths between an elastic layer and two elastic circular punches with neural networks

机译:用神经网络预测弹性层和两个弹性圆形冲头之间的接触长度

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This paper explores the potential use of neural networks (NNs) in the field of contact mechanics. A neural network model is developed for predicting, with sufficient approximation, the contact lengths between the elastic layer and two elastic circular punches. A backpropagation neural network of three layers is employed. First contact problem is solved according to the theory of elasticity with integral transformation technique, and then the results are used to train the neural network. The effectiveness of different neural network configurations is investigated. Effect of parameters such as load factor, elastic punch radii and flexibilities that influence the contact lengths is also explored. The results of the theoretical solution and the outputs generated from the neural network are compared. Results indicate that NN predicted the contact length with high accuracy. It is also demonstrated that NN is an excellent method that can reduce time consumed.
机译:本文探讨了在接触力学领域中神经网络(NNs)的潜在用途。开发了一个神经网络模型,以足够近似的方式预测弹性层和两个弹性圆形冲头之间的接触长度。使用三层的反向传播神经网络。首先根据弹性理论用积分变换技术解决接触问题,然后将结果用于训练神经网络。研究了不同神经网络配置的有效性。还研究了诸如负载系数,弹性冲头半径和柔韧性等参数对接触长度的影响。比较理论解的结果和神经网络产生的输出。结果表明,NN可以高精度地预测接触长度。还证明了NN是一种可以减少消耗时间的优秀方法。

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