Journal of Powder Materials (한국분말재료학회지)

The Korean Powder Metallurgy & Materials Institute (한국분말재료학회 (*구 분말야금학회))
권호 표지
DOI QR코드

DOI QR Code

Correlation of Sintering Parameters with Density and Hardness of Nano-sized Titanium Nitride reinforced Titanium Alloys using Neural Networks

  • Maurya, A.K. (School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University) ;
  • Narayana, P.L (School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University) ;
  • Kim, Hong In (School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University) ;
  • Reddy, N.S. (School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University)
  • Received : 2020.08.22
  • Accepted : 2020.09.14
  • Published : 2020.10.28
Download PDF
⟨ Previous Next ⟩

Abstract

Predicting the quality of materials after they are subjected to plasma sintering is a challenging task because of the non-linear relationships between the process variables and mechanical properties. Furthermore, the variables governing the sintering process affect the microstructure and the mechanical properties of the final product. Therefore, an artificial neural network modeling was carried out to correlate the parameters of the spark plasma sintering process with the densification and hardness values of Ti-6Al-4V alloys dispersed with nano-sized TiN particles. The relative density (%), effective density (g/㎤), and hardness (HV) were estimated as functions of sintering temperature (℃), time (min), and composition (change in % TiN). A total of 20 datasets were collected from the open literature to develop the model. The high-level accuracy in model predictions (>80%) discloses the complex relationships among the sintering process variables, product quality, and mechanical performance. Further, the effect of sintering temperature, time, and TiN percentage on the density and hardness values were quantitatively estimated with the help of the developed model.

Keywords

References

  1. L. Wang, J. Zhang and W. Jiang: Int. J. Refract. Met. Hard Mater., 39 (2013) 103. https://doi.org/10.1016/j.ijrmhm.2013.01.017
  2. L. Wang, W. Jiang, L. Chen, M. Yang and H. Zhu: J. Am. Cream. Soc., 89 (2006) 2364.
  3. Y. I. Lee, J. H. Lee, S. H. Hong and D. Y. Kim: Mater. Res. Bull., 38 (2003) 925. https://doi.org/10.1016/S0025-5408(03)00084-9
  4. W. Liu and M. Naka: Scr. Mater., 48 (2003) 1225. https://doi.org/10.1016/S1359-6462(03)00074-5
  5. L. Gao, H. Z. Wang, J. S. Hong, H. Miyamoto, K. Miyamoto, Y. Nishikawa and S. D. D. L. Torre: J. Eur. Ceram. Soc., 19 (1999) 609. https://doi.org/10.1016/S0955-2219(98)00232-5
  6. Z. A. Munir, U. Anselmi-Tamburini and M. Ohyanagi: J. Mater. Sci., 41 (2006) 763. https://doi.org/10.1007/s10853-006-6555-2
  7. O. Guillon, J. G. Julian, B. Dargatz, T. Kessel, G. Schierning, J. Rathel and M. Herrmann: Adv. Eng. Mater., 16 (2014) 830. https://doi.org/10.1002/adem.201300409
  8. O. E. Falodun, B. A. Obadele, S. R. Oke, M. E. Maja and P. A. Olubambi: J. Alloys Compd., 736 (2018) 202. https://doi.org/10.1016/j.jallcom.2017.11.140
  9. N. S. Reddy, B. B. Panigtahi, M. H. Choi, J. H. Kim and C. S. Lee: Comput. Mater. Sci., 107 (2015) 175. https://doi.org/10.1016/j.commatsci.2015.05.026
  10. G. Xie, Q. Wang, M. Zeng and L. Luo: Appl. Therm. Eng., 27 (2007) 1096. https://doi.org/10.1016/j.applthermaleng.2006.07.036
  11. S. C. Lee: Eng. Struct., 25 (2003) 849. https://doi.org/10.1016/S0141-0296(03)00004-X
  12. N. S. Reddy, Y. H. Lee, C. H. Park and C. S. Lee: Mater. Sci. Eng. A, 492 (2008) 276. https://doi.org/10.1016/j.msea.2008.03.030
  13. A. K. Maurya, P. L. Narayana, A. G. Bhavani and J. K. Hong: J. Electrostat., 104 (2020) 103425. https://doi.org/10.1016/j.elstat.2020.103425
  14. N. S. Reddy, J. Krishnaiah, S. G. Hong and J. S. Lee: Mater. Sci. Eng. A, 508 (2009) 93. https://doi.org/10.1016/j.msea.2008.12.022
  15. N. S. Reddy, J. Krishnaiah, B. Y. Hur and J. S. Lee: Comput. Mater. Sci., 101 (2015) 120. https://doi.org/10.1016/j.commatsci.2015.01.031
  16. C. H. Park, D. Cha, M. Kim, N. S. Reddy and J. T. Yeom: Met. Mater. Int., 25 (2019) 768. https://doi.org/10.1007/s12540-018-00225-8
  17. K. M. Tsai: Int. J. Refract. Met. Hard Mater., 29 (2011) 188. https://doi.org/10.1016/j.ijrmhm.2010.10.006
  18. T. Abdessalem, F. Schoenstein, F. Tetard and M. Abdellaoui: Int. J. Refract. Met. Hard Mater., 30 (2012) 64. https://doi.org/10.1016/j.ijrmhm.2011.06.013