Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 108492, 12 pages
http://dx.doi.org/10.1155/2014/108492
Research Article

A Comparative Study on Improved Arrhenius-Type and Artificial Neural Network Models to Predict High-Temperature Flow Behaviors in 20MnNiMo Alloy

School of Material Science and Engineering, Chongqing University, Chongqing 400044, China

Received 24 August 2013; Accepted 22 December 2013; Published 12 February 2014

Academic Editors: F. Berto and Y.-Y. Chen

Copyright © 2014 Guo-zheng Quan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. H. Wang, Y. F. Li, W. H. Wang, J. Zhou, and A. Chiba, “Quantitative analysis of work hardening and dynamic softening behavior of low carbon alloy steel based on the flow stress,” Materials and Design, vol. 45, pp. 384–392, 2013. View at Google Scholar
  2. Y. C. Lin, M.-S. Chen, and J. Zhong, “Constitutive modeling for elevated temperature flow behavior of 42CrMo steel,” Computational Materials Science, vol. 42, no. 3, pp. 470–477, 2008. View at Publisher · View at Google Scholar · View at Scopus
  3. B. S. Lee, Y. J. Oh, J. H. Yoon, I. H. Kuk, and J. H. Hong, “J-R fracture properties of SA508-1a ferritic steels and SA312-TP347 austenitic steels for Pressurized Water Reactor's (PWR) primary coolant piping,” Nuclear Engineering and Design, vol. 199, no. 1, pp. 113–123, 2000. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Y. Sun, L. H. Hao, S. J. Li, D. Li, and Y. Li, “Modeling flow stress constitutive behavior of SA508-3 steel for nuclear reactor pressure vessels,” Journal of Nuclear Materials, vol. 418, no. 1–3, pp. 269–280, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. S. A. Krishnan, C. Phaniraj, C. Ravishankar, A. K. Bhaduri, and P. V. Sivaprasad, “Prediction of high temperature flow stress in 9Cr-1Mo ferritic steel during hot compression,” International Journal of Pressure Vessels and Piping, vol. 88, no. 11-12, pp. 501–506, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. C. Phaniraj, D. Samantaray, S. Mandal, and A. K. Bhaduri, “A new relationship between the stress multipliers of Garofalo equation for constitutive analysis of hot deformation in modified 9Cr-1Mo (P91) steel,” Materials Science and Engineering A, vol. 528, no. 18, pp. 6066–6071, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Momeni and K. Dehghani, “Characterization of hot deformation behavior of 410 martensitic stainless steel using constitutive equations and processing maps,” Materials Science and Engineering A, vol. 527, no. 21-22, pp. 5467–5473, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. C. Lin and X.-M. Chen, “A critical review of experimental results and constitutive descriptions for metals and alloys in hot working,” Materials and Design, vol. 32, no. 4, pp. 1733–1759, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Cai, F. Li, T. Liu, B. Chen, and M. He, “Constitutive equations for elevated temperature flow stress of Ti-6Al-4V alloy considering the effect of strain,” Materials and Design, vol. 32, no. 3, pp. 1144–1151, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. J. H. Sung, J. H. Kim, and R. H. Wagoner, “A plastic constitutive equation incorporating strain, strain-rate, and temperature,” International Journal of Plasticity, vol. 26, no. 12, pp. 1746–1771, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. F. A. Slooff, J. Zhou, J. Duszczyk, and L. Katgerman, “Constitutive analysis of wrought magnesium alloy Mg-Al4-Zn1,” Scripta Materialia, vol. 57, no. 8, pp. 759–762, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. Y.-C. Lin, M.-S. Chen, and J. Zhang, “Modeling of flow stress of 42CrMo steel under hot compression,” Materials Science and Engineering A, vol. 499, no. 1-2, pp. 88–92, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. P. Changizian, A. Zarei-Hanzaki, and A. A. Roostaei, “The high temperature flow behavior modeling of AZ81 magnesium alloy considering strain effects,” Materials and Design, vol. 39, pp. 384–389, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. O. Sabokpa, A. Zarei-Hanzaki, H. R. Abedi, and N. Haghdadi, “Artificial neural network modeling to predict the high temperature flow behavior of an AZ81 magnesium alloy,” Materials and Design, vol. 39, pp. 390–396, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Mandal, P. V. Sivaprasad, S. Venugopal, and K. P. N. Murthy, “Artificial neural network modeling to evaluate and predict the deformation behavior of stainless steel type AISI 304L during hot torsion,” Applied Soft Computing Journal, vol. 9, no. 1, pp. 237–244, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. A. K. Gupta, S. K. Singh, S. Reddy, and G. Hariharan, “Prediction of flow stress in dynamic strain aging regime of austenitic stainless steel 316 using artificial neural network,” Materials and Design, vol. 35, pp. 589–595, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. G. Z. Quan, W. Q. Lv, Y. P. Mao, Y. W. Zhang, and J. Zhou, “Prediction of flow stress in a wide temperature range involving phase transformation for as-cast Ti-6Al-2Zr-1Mo-1V alloy by artificial neural network,” Materials and Design, vol. 50, pp. 51–61, 2013. View at Google Scholar
  18. Y. Zhu, W. Zeng, Y. Sun, F. Feng, and Y. Zhou, “Artificial neural network approach to predict the flow stress in the isothermal compression of as-cast TC21 titanium alloy,” Computational Materials Science, vol. 50, no. 5, pp. 1785–1790, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. Y. Han, G. J. Qiao, J. P. Sun, and D. N. Zou, “A comparative study on constitutive relationship of as-cast 904L austenitic stainless steel during hot deformation based on Arrhenius-type and artificial neural network models,” Computational Materials Science, vol. 67, pp. 93–103, 2013. View at Google Scholar
  20. X. Xiao, G. Q. Liu, B. F. Hu, X. Zheng, L. N. Wang, and S. J. Chen, “A comparative study on Arrhenius-type constitutive equations and artificial neural network model to predict high-temperature deformation behavior in 12Cr3WV steel,” Computational Materials Science, vol. 62, pp. 227–234, 2012. View at Google Scholar
  21. D. Ponge and G. Gottstein, “Necklace formation during dynamic recrystallization: mechanisms and impact on flow behavior,” Acta Materialia, vol. 46, no. 1, pp. 69–80, 1998. View at Google Scholar · View at Scopus
  22. G.-Z. Quan, Y. Shi, Y.-X. Wang, B.-S. Kang, T.-W. Ku, and W.-J. Song, “Constitutive modeling for the dynamic recrystallization evolution of AZ80 magnesium alloy based on stress-strain data,” Materials Science and Engineering A, vol. 528, no. 28, pp. 8051–8059, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. D. Samantaray, S. Mandal, and A. K. Bhaduri, “A comparative study on Johnson Cook, modified Zerilli-Armstrong and Arrhenius-type constitutive models to predict elevated temperature flow behaviour in modified 9Cr-1Mo steel,” Computational Materials Science, vol. 47, no. 2, pp. 568–576, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. G.-Z. Quan, T.-W. Ku, and B.-S. Kang, “Improvement of formability for multi-point bending process of AZ31B sheet material using elastic cushion,” International Journal of Precision Engineering and Manufacturing, vol. 12, no. 6, pp. 1023–1030, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. H.-Y. Li, D.-D. Wei, Y.-H. Li, and X.-F. Wang, “Application of artificial neural network and constitutive equations to describe the hot compressive behavior of 28CrMnMoV steel,” Materials and Design, vol. 35, pp. 557–562, 2012. View at Publisher · View at Google Scholar · View at Scopus
  26. R. H. Wu, J. T. Liu, H. B. Chang, T. Y. Hsu, and X. Y. Ruan, “Prediction of the flow stress of 0.4C-1.9Cr-1.5Mn-1.0Ni-0.2Mo steel during hot deformation,” Journal of Materials Processing Technology, vol. 116, no. 2-3, pp. 211–218, 2001. View at Publisher · View at Google Scholar · View at Scopus
  27. Y. C. Lin, X. Fang, and Y. P. Wang, “Prediction of metadynamic softening in a multi-pass hot deformed low alloy steel using artificial neural network,” Journal of Materials Science, vol. 43, no. 16, pp. 5508–5515, 2008. View at Publisher · View at Google Scholar · View at Scopus
  28. C. M. Sellars and W. J. McTegart, “On the mechanism of hot deformation,” Acta Metallurgica, vol. 14, no. 9, pp. 1136–1138, 1966. View at Google Scholar · View at Scopus
  29. C. Zener and J. H. Hollomon, “Effect of strain rate upon plastic flow of steel,” Journal of Applied Physics, vol. 15, no. 1, pp. 22–32, 1944. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Mandal, P. V. Sivaprasad, and S. Venugopal, “Capability of a feed-forward artificial neural network to predict the constitutive flow behavior of as cast 304 stainless steel under hot deformation,” Journal of Engineering Materials and Technology, Transactions of the ASME, vol. 129, no. 2, pp. 242–247, 2007. View at Publisher · View at Google Scholar · View at Scopus
  31. Y. C. Lin, J. Zhang, and J. Zhong, “Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel,” Computational Materials Science, vol. 43, no. 4, pp. 752–758, 2008. View at Publisher · View at Google Scholar · View at Scopus
  32. Y. Han, G. Qiao, D. Yan, and D. Zou, “Artificial neural network to predict the hot deformation behavior of super 13Cr martensitic stainless steel,” Materials Science Forum, vol. 695, pp. 361–364, 2011. View at Publisher · View at Google Scholar · View at Scopus