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A comparative study on phenomenon and deep belief network models for hot deformation behavior of an Al–Zn–Mg–Cu alloy

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Abstract

The high temperature deformation behavior of an Al–Zn–Mg–Cu alloy is studied by isothermal compression tests at the temperature range of 573–723 K and strain rate range of 0.001–0.1 s−1. Considering the coupled influences of deformation temperature, strain, and strain rate on hot deformation behavior, a deep belief network (DBN) model, as well as a phenomenological constitutive model, is developed for the studied alloy. In order to validate the developed models, the average absolute relative error and correlation coefficient are evaluated between the measured and predicted true stresses. The results show that the developed DBN model has the better predictability for the high temperature deformation behavior of the studied Al–Zn–Mg–Cu alloy. Moreover, the average absolute relative error and correlation coefficient of DBN model are 0.57% and 0.9997, respectively. In addition, the developed DBN model can be effectively applied in the intelligent manufacturing, such as intelligent isothermal die forging technology.

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References

  1. Y.C. Lin, X.M. Chen, A critical review of experimental results and constitutive descriptions for metals and alloys in hot working. Mater. Des. 32, 1733–1759 (2011)

    Article  Google Scholar 

  2. Y.C. Lin, D.X. Wen, M.S. Chen, X.M. Chen, A novel unified dislocation density based model for hot deformation behavior of a nickel-based superalloy under dynamic recrystallization conditions. Appl. Phys. A 122, 805 (2016)

    Article  ADS  Google Scholar 

  3. H. Jiang, J.X. Dong, M.C. Zhang, L. Zheng, Z.H. Yao, Hot deformation characteristics of alloy 617B nickel-based superalloy: a study using processing map. J. Alloys Compd. 647, 338–350 (2015)

    Article  Google Scholar 

  4. A.A. Khamei, K. Dehghani, Effects of strain rate and temperature on hot tensile deformation of severe plastic deformed 6061 aluminum alloy. Mater. Sci. Eng. A 627, 1–9 (2015)

    Article  Google Scholar 

  5. A.A. Khamei, K. Dehghani, Hot ductility of severe plastic deformed AA6061 aluminum alloy. Acta Metall. Sin. 28, 322–330 (2015)

    Article  Google Scholar 

  6. Y.C. Lin, D.X. Wen, Y.C. Huang, X.M. Chen, X.W. Chen, A unified physically-based constitutive model for describing strain hardening effect and dynamic recovery behavior of a Ni-based superalloy. J. Mater. Res. 30, 3784–3794 (2015)

    Article  ADS  Google Scholar 

  7. H.R. Rezaei Ashtiani, H. Bisadi, M.H. Parsa, Influence of thermomechanical parameters on the hot deformation behavior of AA1070. J. Eng. Mater. Technol. 136, 1–6 (2014)

    Google Scholar 

  8. J. Cai, X.L. Zhang, K.S. Wang, C.P. Miao, Physics-based constitutive model to predict dynamic recovery behavior of BFe10-1-2 cupronickel alloy during hot working. High Temp. Mater. Process. 5, 1037–1045 (2016)

    ADS  Google Scholar 

  9. F. Montheillet, D. Piot, N. Matougui, M.L. Fares, A critical assessment of three usual equations for strain hardening and dynamic recovery. Metall. Mater. Trans. A 45, 4324–4332 (2014)

    Article  Google Scholar 

  10. M.S. Chen, Y.C. Lin, X.S. Ma, The kinetics of dynamic recrystallization of 42CrMo steel. Mater. Sci. Eng. A 556, 260–266 (2012)

    Article  Google Scholar 

  11. H.B. Zhang, K.F. Zhang, H.P. Zhou, Z. Lu, C.H. Zhao, X.L. Yang, Effect of strain rate on microstructure evolution of a nickel-based superalloy during hot deformation. Mater. Des. 80, 51–62 (2015)

    Article  Google Scholar 

  12. C. Zhang, L.W. Zhang, W.F. Shen, M.F. Li, S.D. Gu, Characterization of hot deformation behavior of Hastelloy C-276 using constitutive equation and processing map. J. Mater. Eng. Perform. 24, 149–157 (2015)

    Article  Google Scholar 

  13. S.V. Sajadifar, G.G. Yapici, Elevated temperature mechanical behavior of severely deformed titanium. J. Mater. Eng. Perform. 23, 1834–1844 (2014)

    Article  Google Scholar 

  14. D. Samantaray, S. Mandal, A.K. Bhaduri, Optimization of hot working parameters for thermo-mechanical processing of modified 9Cr–1Mo (P91) steel employing dynamic materials model. Mater. Sci. Eng. A 528, 5204–5211 (2011)

    Article  Google Scholar 

  15. M.A. Davinci, D. Samantaray, U. Borah, S.K. Albert, A.K. Bhaduri, Influence of processing parameters on hot workability and microstructural evolution in a carbon–manganese–silicon steel. Mater. Des. 88, 567–576 (2015)

    Google Scholar 

  16. Y.C. Lin, M.S. Chen, J. Zhong, Constitutive modeling for elevated temperature flow behavior of 42CrMo steel. Comput. Mater. Sci. 42, 470–477 (2008)

    Article  Google Scholar 

  17. H.R.R. Ashtiani, M.H. Parsa, H. Bisadi, Constitutive equations for elevated temperature flow behavior of commercial purity aluminum. Mater. Sci. Eng. A 545, 61–67 (2012)

    Article  Google Scholar 

  18. R. Bobbili, V. Madhu, Constitutive modeling of hot deformation behavior of high-strength armor steel. J. Mater. Eng. Perform. 25, 1829–1838 (2016)

    Article  Google Scholar 

  19. L. Chen, G.Q. Zhao, J. Gong, X.X. Chen, M.M. Chen, Hot deformation behaviors and processing maps of 2024 aluminum alloy in as-cast and homogenized states. J. Mater. Eng. Perform. 24, 5002–5012 (2015)

    Article  Google Scholar 

  20. R.S. Qi, B.F. Guo, X.G. Liu, M. Jin, Flow stress behaviors and microstructure evolution of 300 M high strength steel under isothermal compression. J. Iron Steel Res. Int. 21, 1116–1123 (2014)

    Article  Google Scholar 

  21. Y.C. Lin, K.K. Li, H.B. Li, J. Chen, X.M. Chen, D.X. Wen, New constitutive model for high-temperature deformation behavior of Inconel 718 superalloy. Mater. Des. 74, 108–118 (2015)

    Article  Google Scholar 

  22. Y.H. Liu, Z.K. Yao, Y.Q. Ning, Y. Nan, H.Z. Guo, C. Qin, Z.F. Shi, The flow behavior and constitutive equation in isothermal compression of FGH4096–GH4133B dual alloy. Mater. Des. 63, 829–837 (2014)

    Article  Google Scholar 

  23. J.P. Li, X.S. Xia, Modeling high temperature deformation behavior of large-scaled Mg–Al–Zn magnesium alloy fabricated by semi-continuous casting. J. Mater. Eng. Perform. 24, 3539–3548 (2015)

    Article  Google Scholar 

  24. Z.W. Cai, F.X. Chen, J.Q. Guo, Constitutive model for elevated temperature flow stress of AZ41M magnesium alloy considering the compensation of strain. J. Alloys Compd. 648, 215–222 (2015)

    Article  Google Scholar 

  25. Y.C. Lin, J. Zhang, J. Zhong, Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel. Comput. Mater. Sci. 43, 752–758 (2008)

    Article  Google Scholar 

  26. X.W. Yang, W.Y. Li, Flow behavior and processing maps of a low-carbon steel during hot deformation. Metall. Mater. Trans. A 2015(46), 6052–6064 (2015)

    Article  Google Scholar 

  27. G.Z. Quan, J.T. Liang, W.Q. Lv, D.S. Wu, Y.Y. Liu, G.C. Luo, J. Zhou, A characterization for the constitutive relationships of 42CrMo high strength steel by artificial neural network and its application in isothermal deformation. Mater. Res. 17, 1102–1114 (2014)

    Article  Google Scholar 

  28. G.E. Hinton, S. Osindero, Y.W. Teh, A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  29. K. Chen, A. Salman, Learning speaker-specific characteristics with a deep neural architecture. IEEE Trans. Neural Netw 22, 1744–1756 (2011)

    Article  ADS  Google Scholar 

  30. T. Kuremoto, S. Kimura, K. Kobayashi, M. Obayashi, Time series forecasting using a deep belief network with restricted boltzmann machines. Neurocomputing 137, 47–56 (2014)

    Article  Google Scholar 

  31. Y.C. Lin, J. Li, M.S. Chen, Y.X. Liu, Y.J. Liang, A deep belief network to predict the hot deformation behavior of a Ni-based superalloy. Neural Comput Appl (2017). doi:10.1007/s00521-016-2635-7

    Google Scholar 

  32. Y.C. Lin, L.T. Li, Y.C. Xia, Y.Q. Jiang, Hot deformation and processing map of a typical Al–Zn–Mg–Cu alloy. J. Alloys Compd. 550, 438–445 (2013)

    Article  Google Scholar 

  33. Y.C. Lin, L.T. Li, Y.X. Fu, Y.Q. Jiang, Hot compressive deformation behavior of 7075 Al alloy under elevated temperature. J. Mater. Sci. 47, 1306–1318 (2012)

    Article  ADS  Google Scholar 

  34. R. Bobbili, V. Madhu, A.K. Gogia, Tensile behaviour of aluminium 7017 alloy at various temperatures and strain rates. J. Mater. Res. Technol. 5, 190–197 (2016)

    Article  Google Scholar 

  35. D.N. Zhang, Q.Q. Shangguan, C.J. Xie, F. Liu, A modified Johnson-Cook model of dynamic tensile behaviors for 7075–T6 aluminum alloy. J. Alloys Compd. 619, 186–194 (2015)

    Article  Google Scholar 

  36. D. Trimble, G.E. O’Donnell, Constitutive modelling for elevated temperature flow behaviour of AA7075. Mater. Des. 76, 150–168 (2015)

    Article  Google Scholar 

  37. K. Shojaei, S.V. Sajadifar, G.G. Yapici, On the mechanical behavior of cold deformed aluminum 7075 alloy at elevated temperatures. Mater. Sci. Eng. A 670, 81–89 (2016)

    Article  Google Scholar 

  38. D. Samantaray, S. Mandal, C. Phaniraj, A.K. Bhaduri, Flow behavior and microstructural evolution during hot deformation of AISI Type 316 L(N) austenitic stainless steel. Mater. Sci. Eng. A 528, 8565–8572 (2011)

    Article  Google Scholar 

  39. Y.C. Lin, D.G. He, M.S. Chen, X.M. Chen, C.Y. Zhao, X. Ma, Z.L. Long, EBSD analysis of evolution of dynamic recrystallization grains and δ phase in a nickel-based superalloy during hot compressive deformation. Mater. Des. 97, 13–24 (2016)

    Google Scholar 

  40. A. Momeni, K. Dehghani, Prediction of dynamic recrystallization kinetics and grain size for 410 martensitic stainless steel during hot deformation. Met. Mater. Int. 16, 843–849 (2010)

    Article  Google Scholar 

  41. Y.C. Lin, X.Y. Wu, X.M. Chen, J. Chen, D.X. Wen, J.L. Zhang, L.T. Li, EBSD study of a hot deformed nickel-based superalloy. J. Alloys Compd. 640, 101–113 (2015)

    Article  Google Scholar 

  42. Y.C. Lin, D.X. Wen, J. Deng, G. Liu, J. Chen, Constitutive models for high-temperature flow behaviors of a Ni-Based superalloy. Mater. Des. 59, 115–123 (2014)

    Article  Google Scholar 

  43. Y. Bengio, Learning deep architectures for AI. Found. Trends Mach. Learn. 2, 1–127 (2009)

    Article  MATH  Google Scholar 

  44. G. Hinton, A practical guide to training restricted boltzmann machines, Technical Report, Department of Computer Science, University of Toronto (2010)

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation Council of China (Grant No. 51375502), the Project of Innovation-driven Plan in Central South University (Grant No. 2016CX008), the National Key Basic Research Program (Grant No. 2013CB035801), the Natural Science Foundation for Distinguished Young Scholars of Hunan Province (Grant No. 2016JJ1017), Program of Chang Jiang Scholars of Ministry of Education (No. Q2015140), and the Science and technology leading talent in Hunan Province (Grant No. 2016RS2006), China.

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Lin, Y.C., Liang, YJ., Chen, MS. et al. A comparative study on phenomenon and deep belief network models for hot deformation behavior of an Al–Zn–Mg–Cu alloy. Appl. Phys. A 123, 68 (2017). https://doi.org/10.1007/s00339-016-0683-6

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