Artificial neural network modeling to predict the high temperature flow behavior of an AZ81 magnesium alloy |
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| Affiliation: | 1. National Key Laboratory for Precision Hot Processing of Metals, Harbin Institute of Technology, Harbin 150001, China;2. China Nuclear Industry 23 Construction Co., Ltd., Beijing 101300, China;1. The Complex Laboratory of Hot Deformation and Thermomechanical Processing of High-Performance Engineering Materials, School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran, Tehran, Iran;2. School of Mechanical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran;3. School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran, Tehran, Iran;1. College of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, China;2. The MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions, Northwestern Polytechnical University, Xi''an 710129, China;3. State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi''an 710072, China;1. UNESP – Univ. Estadual Paulista, Laboratório de Anelasticidade e Biomateriais, 17.033.360, Bauru, SP, Brazil;2. IBTN – Br, Institute of Biomaterials, Tribocorrosion and Nanomedicine, Brazilian Branch, 17.033.360, Bauru, SP, Brazil;3. IFSP – Federal InstituteofEducation, Science and Technology of São Paulo, Grupo de Pesquisa em Materiais Metálicos Avançados, 18095-410, Sorocaba, SP, Brazil |
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| Abstract: | In the present work, the capability of artificial neural network (ANN) has been evaluated to describe and to predict the high temperature flow behavior of a cast AZ81 magnesium alloy. Toward this end, a set of isothermal hot compression tests were carried out in temperature range of 250–400 °C and strain rates of 0.0001, 0.001 and 0.01 s−1 up to a true strain of 0.6. The flow stress was primarily predicted by the hyperbolic laws in an Arrhenius-type of constitutive equation considering the effects of strain, strain rate and temperature. Then, a feed-forward back propagation artificial neural network with single hidden layer was established to investigate the flow behavior of the material. The neural network has been trained with an in-house database obtained from hot compression tests. The performance of the proposed models has been evaluated using a wide variety of statistical indices. The comparative assessment of the results indicates that the trained ANN model is more efficient and accurate in predicting the hot compressive behavior of cast AZ81 magnesium alloy than the constitutive equations. |
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