Skip to main content
SpringerLink
Log in

Establishment of constitutive relationship model for 2519 aluminum alloy based on BP artificial neural network

  • Published:
Journal of Central South University of Technology Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Kramer L S, Blair T P, Blough S D. Stress-corrosion cracking susceptibility of various product forms of aluminum alloy 2519 [J]. Journal of Materials Engineering and Performance, 2002, 11(6): 645–650.

    Article  Google Scholar 

  2. Devicent S M, Devletian J H, Gedon S A. Weld properties of the newly developed 2519 - T87 aluminum armor alloy [J]. Welding Journal, 1988, 67(7): 33–43.

    Google Scholar 

  3. Dymek S, Dollar M. TEM investigation of age-hardenable Al 2519 alloy subjected to stress corrosion cracking tests[J]. Materials Chemistry and Physics, 2003, 82(1): 1–3.

    Article  Google Scholar 

  4. Lee W S, Lam H F. The deformation behavior and microstructure evolution of high strength alloy steel at high rate of strain[J]. Materials Processing Technology, 1996, 57(3–4): 233–240.

    Article  Google Scholar 

  5. Lee W S, Lin C F. High temperature deformation behavior of Ti6Al4V alloy evaluated by high strain rate compression tests[J]. Journal of Materials Processing Technology, 1998, 75(1–3): 127–136.

    Article  Google Scholar 

  6. Rao K P, Prased Y K. Neural network approach to flow stress evaluation in hot deformation[J]. Journal of Materials Processing Technology, 1995, 53(3–4): 552–566.

    Article  Google Scholar 

  7. LIU Jian-tao, CHANG Hong-bing, XU Zu-yao, et al. Prediction of the flow stress of high-speed steel during hot deformation using a BP artificial neural network[J]. Journal of Materials Processing Technology, 2000, 103(2): 200–205.

    Article  Google Scholar 

  8. Rao K P, Haobolt E B. Development of constitutive relationship using compression testing of medium carbon steel[J]. Journal of Engineering Materials and Technology, 1992, 114(1): 116–123.

    Article  Google Scholar 

  9. ZHANG Xing-quan, PENG Ying-hong, RUAN Xue-yu. An artificial neural network model for Ti-17 alloys[J]. The Chinese Journal of Nonferrous Metals, 1999, 9(3): 590–595. (in Chinese)

    Google Scholar 

  10. Liu Z Y, Wang W D, Gao W. Prediction of the mechanical properties of hot-rolled C-Mn steels using artificial neural networks[J]. Journal of Materials Processing Technology, 1996, 57(4): 332–336.

    Article  Google Scholar 

  11. Larkiola J, Myllykoski P, Korhonen A S. The role of neural networks in the optimization of rolling processes[J]. Journal of Materials Processing Technology, 1998, 80–81(1): 16–23.

    Article  Google Scholar 

  12. WANG Xue-ye, QIU Guan-zhou, WAN Dian-zuo. Molten salt phase diagrams calculation using artificial neural network or pattern recognition bond parameters[J]. Trans Nonferrous Met Soc China, 1998, 8(1): 142–148.

    Google Scholar 

  13. FENG Jian-peng, GUO Ling. The application of artificial neural network model to constitutive relations of heat-resistant alloys[J]. Journal of China Mechanic Engineering, 1999, 10(1): 49–51. (in Chinese)

    Google Scholar 

  14. QI Le-hua, HOU Jun-jie. An neural network model for direct extrusion in metal solidification [J]. The Chinese Journal of Nonferrous Metals, 1999, 9(3): 586–589. (in Chinese)

    Google Scholar 

  15. CHAI Tian-ruo, XIE Shu-ming, DU Bing, et al. An artificial network model for the terminus temperature and carbon content in steel metallurgy[J]. The Chinese Journal of Nonferrous Metals, 1999, 9 (4): 868–872. (in Chinese)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Xiangtan University, 411105, Xiangtan, China

    Lin Qi-quan PhD (Associate professor)

  2. School of Materials Science and Engineering, Central South University, 410083, Changsha, China

    Lin Qi-quan PhD (Associate professor), Peng Da-shu & Zhu Yuan-zhi

Authors
  1. Lin Qi-quan PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peng Da-shu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhu Yuan-zhi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lin Qi-quan PhD.

Additional information

Foundation item: Project(03-5) supported by the State Key Lab for Plastic Forming Simulation and Die Technique

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lin, Qq., Peng, Ds. & Zhu, Yz. Establishment of constitutive relationship model for 2519 aluminum alloy based on BP artificial neural network. J Cent. South Univ. Technol. 12, 380–384 (2005). https://doi.org/10.1007/s11771-005-0165-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-005-0165-z

Key words

CLC number

Search

Navigation