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Prediction model on flow stress of 6061 aluminum alloy sheet based on GA-BP and PSO-BP neural networks
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Affiliation:

College of Mechanical and Electronic engineering,Nanjing Forestry University

Clc Number:

TG146.2

Fund Project:

Natural Science Foundation of Jiangsu Higher Education Institutions of China(18KJB460020), High-level (Higher education) Science Foundation of Nanjing Forestry University(GXL2018020) and the Youth Science and Technology Innovation Foundation of Nanjing Forestry University(CX2018027).

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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.

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[Feng-juan Ding, Xiang-dong Jia, Teng-jiao Hong, 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.]
DOI:10.12442/j. issn.1002-185X.20190884

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History
  • Received:October 26,2019
  • Revised:May 01,2020
  • Adopted:December 10,2019
  • Online: July 09,2020
  • Published: