Using GA–ANN to Optimize Heat Treatment Technological Parameters of Super-Martensitic Stainless Steel

Huan Liu; Jun Hui Yu; Duo Wang; De Ning Zou

doi:10.4028/www.scientific.net/MSF.695.401

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HomeMaterials Science ForumMaterials Science Forum Vol. 695Using GA–ANN to Optimize Heat Treatment...

Using GA–ANN to Optimize Heat Treatment Technological Parameters of Super-Martensitic Stainless Steel

Abstract:

In this investigation a theoretical model based on artificial neural network (ANN) and genetic algorithm (GA) have been developed to optimize the heat treatment technological parameters for achieving the excellent mechanical property of super-martensitic stainless steel (SMSS). The ANN was used to correlate the heat treatment technological conditions to the mechanical property. The GA and ANN were incorporated to find the optimal technological parameters. The result shows that the most optimal heat treatment technological is 1003.9°C×0.5h (air cooling) +629.75°C+2.06h (air cooling). By comparing the prediction values with the experimental data it is demonstrated that the combined GA–ANN algorithm is efficient and strong method to find the optimum heat treatment technological for producing super-martensitic stainless steel.

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Materials Science Forum (Volume 695)

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401-404

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https://doi.org/10.4028/www.scientific.net/MSF.695.401

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July 2011

Authors:

Huan Liu, Jun Hui Yu, Duo Wang, De Ning Zou

Keywords:

Artificial Neural Network (ANN), Genetic Algorithm (GA), Heat Treatment, Super-Martensitic Stainless Steel

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References

[1] C.A.D. Rodrigues, P.L.D. Lorenzo, A. Sokolowski, et al: Materials Science and Engineering A. Vol. 460–461 (2007), p.149–152.

Google Scholar

[2] ZOU Dening, HAN Ying, ZHANG Wei, et al: Journal of Iron and Steel Research, International. Vol. 17(8) (2010), pp.50-54.

Google Scholar

[3] B. Qin, Z.Y. Wang and Q.S. Sun: Materials Characterization. Vol. 59(2008), p.1096 – 1100.

Google Scholar

[4] M. Yazdanmehr, S.H. Mousavi Anijdan and A. Bahrami: Computational Materials Science. Vol. 44 (2009), p.1218–1221.

Google Scholar

[5] Zhiyu Chen, Dening Zou, Junhui Yu et al: Materials Science Forum. Vol. 658 (2010), pp.145-148.

Google Scholar

[6] Junhui Yu, Dening Zou, Ying Han, et al: Materials Science Forum. Vol. 658 (2010), pp.141-144.

Google Scholar

[7] H. Mirzadeh and A. Najafizadeh: Materials characterization. Vol. 59 (2008), pp.1650-1654.

Google Scholar

[8] S.H. Mousavi Anijdan, A. Bahrami, H.R. Madaah Hosseini, et al: Materials and Design. Vol. 27 (2006), p.605–609.

Google Scholar

[9] L. Aijun, L. Hejun, L. Kezhi, et al: Acta Materials. Vol. 52 (2004), p.299–305.

Google Scholar