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Knowledge-based artificial neural network model to predict the properties of alpha+ beta titanium alloys

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Abstract

In view of emerging applications of alpha+beta titanium alloys in aerospace and defense, we have aimed to develop a Back propagation neural network (BPNN) model capable of predicting the properties of these alloys as functions of alloy composition and/or thermomechanical processing parameters. The optimized BPNN model architecture was based on the sigmoid transfer function and has one hidden layer with ten nodes. The BPNN model showed excellent predictability of five properties: Tensile strength (r: 0.96), yield strength (r: 0.93), beta transus (r: 0.96), specific heat capacity (r: 1.00) and density (r: 0.99). The developed BPNN model was in agreement with the experimental data in demonstrating the individual effects of alloying elements in modulating the above properties. This model can serve as the platform for the design and development of new alpha+beta titanium alloys in order to attain desired strength, density and specific heat capacity.

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References

  1. R. Pitler and A. Hurlich, Some mechanical and ballistic properties of titanium and titanium alloys, Report#401/17, Watertown Arsenal Laboratory (1950).

    Google Scholar 

  2. J. Sliney, Status and potential of titanium armor, Proceedings of the metallurgical advisory committee on rolled armor, AMRA MS 64-04, U.S. Army Materials Research Agency (1964).

    Google Scholar 

  3. M. Donachie, Titanium: A technical guide, ASM International, Metals Park, OH (1989).

    Google Scholar 

  4. M. S. Burkins, W. Love and J. R. Wood, Effect of annealing temperature on the ballistic limit velocity of Ti-6Al-4V ELI, Report ARL-MR-359, U.S. Army Research Laboratory (1997).

    Google Scholar 

  5. C. Zheng, F.Wang, X. Cheng, J. Liu, K. Fu, T. Liu, Z. Zhu, K. Yang, M. Peng and D. Jin, Failure mechanisms in ballistic performance of Ti-6Al-4V targets having equiaxed and lamellar microstructures, Inter. J.Imp. Eng., 85 (2015) 161–169.

    Article  Google Scholar 

  6. I. Inagaki, T. Takechi, Y. Shirai and N. Ariyasu, Application and features of titanium for the aerospace industry, Nippon Steel & Sumitomo Metal Technical Report No. 106 (2014).

    Google Scholar 

  7. R. K. Gupta, V. A. Kumar and S. Chhangani, Study on variants of solution treatment and aging cycle of titanium alloy Ti6Al4V, J. Mater. Eng. Perf. (2016) (In press).

    Google Scholar 

  8. I. Sacristan, A. Garay, E. Hormaetxe, J. Aperribay and P. J. Arrazola, Influence of oxygen content on the machinability of Ti-6Al-4V alloy, Inter.J. Adva. Manu. Tech. (2016) (In press).

    Google Scholar 

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

    Google Scholar 

  10. S. Ankem, G. K. Scarr, I. L. Caplan, J. C. Williams, S. R. Sergle, H. B. Bomberger, P. Lacombe, R. Tricot and G. Beranger, Multiple regression analysis of the effects of various alloying elements on the properties of titanium alloys, Proceeding of 6 th World Conference on Titanium Cannes, France, Societe Francaise de Metallurgie (1989) 265–268.

    Google Scholar 

  11. R. P. Lippmann, An introduction to computing with neural network, IEEE ASSP Mag. (1987) 36–54.

    Google Scholar 

  12. Y. Sun, W. Zeng, Y. Han, Y. Zhao, G. Wang, M. S. Dargusch and P. Guo, Modeling the correlation between microstructure and the properties of the Ti–6Al–4V alloy based on an artificial neural network, Mater. Sci. Eng. A, 528 (29-30) (2011) 8757–8764.

    Article  Google Scholar 

  13. Z. Guo, S. Malinov and W. Sha, Modelling beta transus temperature of titanium alloys using artificial neural network, Comput. Mater. Sci., 32 (1) (2005) 1–12.

    Article  Google Scholar 

  14. S. Malinov and W. Sha, Application of artificial neural networks for modelling correlations in titanium alloys, Mater. Sci. Eng. A, 365 (1–2) (2004) 202.

    Article  Google Scholar 

  15. S. Malinov, W. Sha and J. J. McKeown, Modelling the correlation between processing parameters and properties in titanium alloys using artificial neural network, Comput. Mat. Sci., 21 (3) (2001) 375.

    Article  Google Scholar 

  16. S. Malinov, W. Sha and Z. Guo, Application of artificial neural network for prediction of time-temperaturetransformation diagrams in titanium alloys, Mater. Sci. Eng. A, 283 (1–2) (2000) 1.

    Article  Google Scholar 

  17. P. S. Noori Banu and S. Devaki Rani, Development of an artificial neural network model to predict the properties of alpha and near alpha titanium alloys as a function of their composition, Inter. J. Innov. Res. Sci. Eng. Tech., 3 (4) (2014).

    Google Scholar 

  18. Titanium alloy guide, RTI International metals Inc. (2000).

  19. J. C.-M. Li, Microstructure and properties of materials, World Scientific, 2 (1996).

    Google Scholar 

  20. D. C. Hofmann, J. Y. Suh, A. Wiest, M. L. Lind, M. D. Demetriou and W. L. Johnson, Development of tough, lowdensity titanium-based bulk metallic glass matrix composites with tensile ductility, Proc. Natl. Acad. Sci., USA, 105 (51) (2008) 20136–20140.

    Article  Google Scholar 

  21. A. M. Tavares, W. S. Ramos, J. C. de Blas, E. S. Lopes, R. Caram, W. W. Batista and S. A. Souza, Influence of Si addition on the microstructure and mechanical properties of Ti-35Nb alloy for applications in orthopedic implants, J. Mech. Behav. Biomed. Mater, 51 (2015) 74–87.

    Article  Google Scholar 

  22. A. Zhecheva, W. Sha, S. Malinov and A. Long, Enhancing the microstructure and properties of titanium alloys through nitriding and surface engineering methods, Surface & Coatings Technology, 200 (2005) 2192–2207.

    Article  Google Scholar 

  23. A. Corona, Characterization of the relationship between the microstructure and tensile strength of annealed Ti-6Al-4V, California Polytechnic State University, San Luis Obispo, Project report (2011).

    Google Scholar 

  24. S. Malinov and W. Sha, The neural network modeling of titanium alloy phase transformation and mechanical properties, JOM (2005) 54–57.

    Google Scholar 

  25. N. S. Reddy, C. S. Lee, J. H. Kim and 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-Struct. Mater, 434 (1) (2006) 218–226.

    Article  Google Scholar 

  26. N. S. Reddy, H. J. Choi and H. B. Young, Practical model for predicting beta transus temperature of titanium alloys, Kor. J. Mater. Res., 24 (7) (2014) 381–338.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Metallurgical Engineering, Jawaharlal Nehru Technological University, Hyderabad, 500085, India

    P. S. Noori Banu & S. Devaki Rani

Authors
  1. P. S. Noori Banu
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  2. S. Devaki Rani
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Correspondence to P. S. Noori Banu.

Additional information

Recommended by Associate Editor Jin Weon Kim

P. S. Noori Banu did her M.Tech. (Industrial Metallurgy) from Indian Institute of Technology, Roorkee in 2002. Since then, she has been working as a faculty in various capacities in many esteemed institutions. She is currently pursuing her Ph.D. from JNTU-Hyderabad, India.

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Noori Banu, P.S., Devaki Rani, S. Knowledge-based artificial neural network model to predict the properties of alpha+ beta titanium alloys. J Mech Sci Technol 30, 3625–3631 (2016). https://doi.org/10.1007/s12206-016-0723-3

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  • DOI: https://doi.org/10.1007/s12206-016-0723-3

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