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Metals 2017, 7(4), 114;

Modeling the Constitutive Relationship of Al–0.62Mg–0.73Si Alloy Based on Artificial Neural Network

1,* , 1
Key Laboratory of Advanced Structural Materials, Ministry of Education, Changchun University of Technology, Changchun 130012, China
National Key Laboratory for Precision Hot Processing of Metals, Harbin Institute of Technology, Harbin 150001, China
Author to whom correspondence should be addressed.
Academic Editors: Myoung-Gyu Lee and Yannis P. Korkolis
Received: 18 February 2017 / Revised: 16 March 2017 / Accepted: 23 March 2017 / Published: 26 March 2017
(This article belongs to the Special Issue Advances in Plastic Forming of Metals)
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In this work, the hot deformation behavior of 6A02 aluminum alloy was investigated by isothermal compression tests conducted in the temperature range of 683–783 K and strain-rate range of 0.001–1 s−1. According to the obtained true stress–true strain curves, the constitutive relationship of the alloy was revealed by establishing the Arrhenius-type constitutive model and back-propagation (BP) neural network model. It is found that the flow characteristic of 6A02 aluminum alloy is closely related to deformation temperature and strain rate, and the true stress decreases with increasing temperatures and decreasing strain rates. The hot deformation activation energy is calculated to be 168.916 kJ mol−1. The BP neural network model with one hidden layer and 20 neurons in the hidden layer is developed. The accuracy in prediction of the Arrhenius-type constitutive model and BP neural network model is eveluated by using statistics analysis method. It is demonstrated that the BP neural network model has better performance in predicting the flow stress. View Full-Text
Keywords: deformation behavior; constitutive model; BP neural network; aluminum alloy deformation behavior; constitutive model; BP neural network; aluminum alloy

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Han, Y.; Yan, S.; Sun, Y.; Chen, H. Modeling the Constitutive Relationship of Al–0.62Mg–0.73Si Alloy Based on Artificial Neural Network. Metals 2017, 7, 114.

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