Artificial neural network to predict the degraded mechanical properties of metallic materials due to the presence of hydrogen
Artificial neural network to predict the degraded mechanical properties of metallic materials due to the presence of hydrogen
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DOI：
10.1016/j.ijhydene.2017.09.149
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发表时间：
2017-11-23
影响因子：
7.2
通讯作者：
Jothi, Sathiskumar
中科院分区：
文献类型：
--
作者：
Thankachan, Titus;Prakash, K. Soorya;Jothi, Sathiskumar
引入机器学习模型来开发金属材料中由于氢的存在而导致的元素组成和机械性能下降之间的关系。基于人工神经网络的机器学习模型，分别采用单层和多层前馈-反向传播算法对充氢金属材料的力学性能进行预测。采用多层前馈反向传播模型预测拉伸强度，网络拓扑结构为12-13-3-2。采用单层前馈反向传播模型预测延伸率，网络拓扑结构为12-11-1。使用未知输入对开发的模型进行了验证和测试，并研究了它们的能力。使用平均绝对值（MAE）对模型进行评价，并绘制散点图以证明模型的有效性。这两个模型的R值似乎证明了模型已经准备好在实际应用中使用。(C)2017年氢能出版有限责任公司。由爱思唯尔有限公司出版。保留所有权利。
Machine learning models were introduced to develop a relationship between the elemental composition and degraded mechanical properties in metallic materials due to the presence of hydrogen. Single layer and multilayer feed forward back propagation algorithm was developed as artificial neural network based machine learning models to predict the mechanical properties of hydrogen charged metallic materials. Multilayer feed forward back propagation model was used to predicts the tensile strength, had a network topology of 12-13-3-2. And the single layer feed forward back propagation model was employed to predict the percentage of elongation, has a network topology of 12-11-1. The developed models were validated and tested with unknown inputs and their capability was studied. The models were evaluated using Mean Absolute (MAE) value and represented the scatter diagram to demonstrate the efficiency of the models. The R-value for both the models seems to prove that the models are ready to be used in the practical applications. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.