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丁凤娟,贾向东,洪腾蛟,徐幼林.基于GA-BP和PSO-BP神经网络的6061铝合金板材流变应力预测模型[J].稀有金属材料与工程(英文),2020,49(6):1840~1853.[Feng-juan Ding,Xiang-dong Jia,Teng-jiao Hong and You-lin Xu.Prediction model on flow stress of 6061 aluminum alloy sheet based on GA-BP and PSO-BP neural networks[J].Rare Metal Materials and Engineering,2020,49(6):1840~1853.]
Prediction model on flow stress of 6061 aluminum alloy sheet based on GA-BP and PSO-BP neural networks
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Received:October 26, 2019  Revised:May 01, 2020
DOI:
Key words: 6061Aluminum Alloy  Flow stress  Artificial neural network  Genetic algorithm  Particle swarm optimization  Heat treatment Process
Foundation item:江苏省高等学校自然科学基金(18KJB460020),南京林业大学高水平(高等教育)科学基金(GXL2018020)和南京林业大学青年科技创新基金(CX2018027)
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Feng-juan Ding,Xiang-dong Jia,Teng-jiao Hong and You-lin Xu  
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Abstract:
      6061 aluminum alloy, as a kind of heat strengthened aluminum alloy, has good formability, but its plastic flow stress is greatly affected by the final heat treatment parameters, such as heating temperature,holding time and cooling method. Therefore, taking 6061-T6 aluminum alloy cold-rolled sheet as the research object, the plastic deformation behavior of 6061 aluminum alloy under different heat treatment temperatures (500 °C, 530 °C, 560 and 590 °C) were analyzed through uniaxial tensile test, metallographic test and microhardness test. Combined with experimental data and BP, GA-BP and PSO-BP neural networks, the constitutive models of this material under different heat treatment temperature conditions were constructed. The results show that BP, GA-BP and PSO-BP neural network models can better fit the flow behavior of 6061 aluminum alloy under different heat treatment temperature conditions, but PSO-BP neural network model has higher prediction accuracy and performs well in predicting the flow stress of 6061 aluminum alloy , its average absolute error (MAE), average relative error (AARE) and the correlation coefficient (R2) are 1.89, 1.56% and 0.9965, respectively.