| Establishment of constitutive relationship model for 2519 aluminum alloy based on BP artificial neural network |
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| 引用本文： | 林启权 彭大暑 朱远志. Establishment of constitutive relationship model for 2519 aluminum alloy based on BP artificial neural network[J]. 中南工业大学学报(英文版), 2005, 12(4): 380-384. DOI: 10.1007/s11771-005-0165-z |
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| 作者姓名： | 林启权 彭大暑 朱远志 |
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| 作者单位： | [1]School of Mechanical Engineering, Xiangtan University, Xiangtan 411105, China [2]School of Materials Science and Engineering, Central South University, Changsha 410083, China |
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| 摘 要： | An isothermal compressive experiment using Gleeble 1500 thermal simulator was studied to acquire flow stress at different deformation temperatures, strains and strain rates. The artificial neural networks with the error back propagation（BP） algorithm was used to establish constitutive model of 2519 aluminum alloy based on the experiment data. The model results show that the systematical error is small（δ=3.3%） when the value of objective function is 0.2, the number of nodes in the hidden layer is 5 and the learning rate is 0.1. Flow stresses of the material under various thermodynamic conditions are predicted by the neural network model, and the predicted results correspond with the experimental results. A knowledge-based constitutive relation model is developed.
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| 关 键 词： | 铝合金 人工神经网络 运算法则 人工智能 关系模型 |
| 文章编号： | 1005-9784（2005）04-0380-05 |
| 收稿时间： | 2005-02-26 |
| 修稿时间： | 2005-03-05 |
Establishment of constitutive relationship model for 2519 aluminum alloy based on BP artificial neural network |
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| Lin Qi-quan , Peng Da-shu and Zhu Yuan-zhi. Establishment of constitutive relationship model for 2519 aluminum alloy based on BP artificial neural network[J]. Journal of Central South University of Technology, 2005, 12(4): 380-384. DOI: 10.1007/s11771-005-0165-z |
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| Authors: | Lin Qi-quan Peng Da-shu Zhu Yuan-zhi |
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| Affiliation: | (1) School of Mechanical Engineering, Xiangtan University, 411105 Xiangtan, China;(2) School of Materials Science and Engineering, Central South University, 410083 Changsha, China |
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| Abstract: | An isothermal compressive experiment using Gleeble 1500 thermal simulator was studied to acquire flow stress at different deformation temperatures, strains and strain rates. The artificial neural networks with the error back propagation(BP) algorithm was used to establish constitutive model of 2519 aluminum alloy based on the experiment data. The model results show that the systematical error is small(δ=3.3%) when the value of objective function is 0.2, the number of nodes in the hidden layer is 5 and the learning rate is 0.1. Flow stresses of the material under various thermodynamic conditions are predicted by the neural network model, and the predicted results correspond with the experimental results. A knowledge-based constitutive relation model is developed. |
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| Keywords: | 2519 aluminum alloy BP algorithm neural network constitutive model |
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