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首页> 外文期刊>IEEE Transactions on Magnetics >A Supervised Artificial Neural Network-Assisted Modeling of Magnetorheological Elastomers in Tension–Compression Mode
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A Supervised Artificial Neural Network-Assisted Modeling of Magnetorheological Elastomers in Tension–Compression Mode

机译：张力压缩模式中磁流变弹性体的监督人工神经网络辅助建模

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摘要

Modeling of highly sophisticated behavior of magnetorheological elastomers (MREs) is an essential step toward optimally designing and effectively controlling the smart material-based devices. While modeling MREs in shear mode has been widely carried out by employing continuum mechanics, mathematical techniques, and phenomenological approaches, the correct determination of dynamic behavior of MREs in tension–compression mode has been addressed in only a few studies due to inherent complexities mainly arising from the computational demandingness of the process. This article addresses the functionality of artificial neural network (ANN) for prediction of MRE’s dynamic behavior in tension–compression mode under different levels of strain, frequency, and magnetic flux density. A multilayer perceptron-based feed-forward neural network with backpropagation training technique was used with various structures to identify an optimal configuration. A neural network structure with 20 neurons in the hidden layer was adopted, which revealed the mean square error (MSE) magnitude of 7.1 kPa with $R^{2}$ values above 0.97. Afterward, the predicting capacity of the model was evaluated using experimental data sets. The obtained results are suggestive of reasonably acceptable performance of the proposed ANN model, which holds the capacity for a close mapping of the predicted tension–compression stress values to those of experimental ones. Further development of the proposed ANN model serves as a promising approach to deal with the modeling and controlling of engineering devices equipped with tension–compression MREs.
机译：磁流变弹性体（MRE）高度复杂行为的建模是朝向最佳设计和有效地控制基于智能材料的设备的基本步骤。在通过采用连续内的力学，数学技术和现象学方法来建模的剪切模式中的MRES已经广泛地进行，在避免 - 压缩模式中的正确测定张力 - 压缩模式的动态行为已经在仅导致的内部复杂性，这是主要出现的从过程的计算苛刻性。本文涉及人工神经网络（ANN）的功能，用于在不同水平的应变，频率和磁通密度下预测MRE在张力压缩模式下的动态行为。基于多层的Perceptron的前馈神经网络具有背部化训练技术，各种结构用于识别最佳配置。采用隐藏层中具有20个神经元的神经网络结构，揭示了7.1kPa的平均方误差（MSE）幅度<内联公式XMLNS：MML =“http://www.w3.org/1998/math/mathml”xmlns：xlink =“http://www.w3.org/1999/xlink”> $ r ^ {2} $ 值高于0.97。之后，使用实验数据集评估模型的预测能力。所获得的结果旨在提出所提出的ANN模型的合理可接受的性能，该模型可容纳预测的张力压力值与实验结果的能力。建议的ANN模型的进一步发展是一种有希望的方法，可以处理配备张力压缩MRE的工程设备的建模和控制。

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