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Artificial neural network modeling of phase volume fraction of Ti alloy under isothermal and non-isothermal hot forging conditions

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Abstract

An artificial neural network (ANN) model was applied to simulate the phase volume fraction of titanium alloy under isothermal and non-isothermal hot forging condition. For isothermal hot forging process, equilibrium phase volume fraction at specific temperature was predicted. For this purpose, chemical composition of six alloy elements (i.e. Al, V, Fe, O, N, and C) and specimen temperature were chosen as input parameter. After that, phase volume fraction under non-isothermal condition was simulated again. Input parameters consist of initial phase volume fraction, equilibrium phase volume fraction at specific temperature, cooling rate, and temperature. The ANN model was coupled with the FE simulation in order to predict the variation of phase volume fraction during non-isothermal forging. Ti−6A1−4V alloy was forged under isothermal and non-isothermal condition and then, the resulting microstructures were compared with simulated data.

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References

  1. E. W. Collings, The Physical Metallurgy of Titanium Alloys, American Society for Metals, Metals Park, OH 44073, (1984).

    Google Scholar 

  2. G. Terlinde, and G. Fischer, Beta titanium alloys, in Christoph Leyens and Manfred Peters (eds), Titanium and Titanium Alloys, WILEY VCH GmbH & Co. KGaA, (2003) 37–57.

  3. R. A. Wood, Titanium Alloys Handbook, Metals and Ceramics Information Center, Battelle, (1972).

    Google Scholar 

  4. E. J. Dayhoff, Neural Network Architecture: An Introduction, VNR Press, New York, (1990).

    Google Scholar 

  5. J. M. Zurada, Introduction to Artificial Neural Systems, PWS Publishing Company, Boston, (1992).

    Google Scholar 

  6. R. Castro, L. Seraphin,Mem. Sci. Rev. Metall. 63 (1966) 1025–1058.

    Google Scholar 

  7. Y. T. Lee, G. Welsch, Young’s modulus and damping of Ti-6A1-4V alloy as a function of heat treatment and oxygen concentration,Mater. Sci. Eng. A. 128 (1990) 77–89.

    Article  Google Scholar 

  8. M. Jain, M.C. Chaturvedi, N.L. Richards, N.C. Goel, Microstructural characteristics in a phase during superplastic deformation of Ti-6A1-4V,Mater. Sci. Eng. A. 145 (1991) 205–214.

    Article  Google Scholar 

  9. S. Malinov, P. Markovsky, W. Sha, and Z. Guo, Resistivity study and computer modelling of the isothermal transformation kinetics of Ti-6A1-4V and Ti-6Al-2Sn-4Zr-2Mo-0.08Si alloys,Journal of Alloys and Compounds. 314 (2001) 181–192.

    Article  Google Scholar 

  10. N.S. Reddy, C.S. Lee, J.H. Kim, S.L. Semiatin, Determination of the beta-approach curve and beta-transus temperature for titanium alloys using sensitivity analysis of a trained neural network,Mater. Sci. Eng. A. 434 (2006) 218–226.

    Article  Google Scholar 

  11. M.L. Meier, D.R. Lesuer, A.K. Mukherjee, The effects of the α/β phase proportion on the superplasticity of Ti-6A1-4V and iron-modified Ti-6A1-4V,Mater. Sci. Eng. A. 154 (1992), 165–173.

    Article  Google Scholar 

  12. S.L. Semiatin, F. Montheillet, G. Shen, J.J. Jonas, Self-consistent modeling of the flow behavior of wrought alpha/beta titanium alloys under isothermal and nonisothermal hot-working conditions,Metall. Mater Trans. A. 33 (2002) 2719–2727.

    Article  Google Scholar 

  13. R. Boyer, G. Welsch, E.W. Collings, Material Properties Handbook: Titanium Alloys, ASM International, Materials Park, OH 44073, (1994).

    Google Scholar 

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Authors and Affiliations

  1. Korea Institute of Machinery and Materials, 66 Sangnam-Dong, Changwon-City, South Korea

    J. H. Kim, J. T. Yeom & N. K. Park

  2. Department of Materials Science and Engineering, Pohang University of Science and Technology, 790-784, Pohang, Republic of Korea

    N. S. Reddy & C. S. Lee

Authors
  1. J. H. Kim
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  2. N. S. Reddy
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  3. J. T. Yeom
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  4. C. S. Lee
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  5. N. K. Park
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Correspondence to J. H. Kim.

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Kim, J.H., Reddy, N.S., Yeom, J.T. et al. Artificial neural network modeling of phase volume fraction of Ti alloy under isothermal and non-isothermal hot forging conditions. J Mech Sci Technol 21, 1560–1565 (2007). https://doi.org/10.1007/BF03177375

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  • DOI: https://doi.org/10.1007/BF03177375

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