Flow Stress Prediction of High-Nb TiAl Alloys under High Temperature Deformation

Liang Cheng; Hui Chang; Bin Tang; Hong Chao Kou; Jin Shan Li

doi:10.4028/www.scientific.net/AMR.510.723

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Flow Stress Prediction of High-Nb TiAl Alloys under High Temperature Deformation

Abstract:

In this work, a back propagation artificial neural network (BP-ANN) model is conducted to predict the flow behaviors of high-Nb TiAl (TNB) alloys during high temperature deformation. The inputs of the neural network are deformation temperature, log strain rate and strain whereas flow stress is the output. There is a single hidden layer with 7 neutrons in the network, and the weights and bias of the network were optimized by Genetic Algorithm (GA). The comparison result suggests a very good correlation between experimental and predicted data. Besides, the non-experimental flow stress predicted by the network is shown to be in good agreement with the results calculated by three dimensional interpolation, which confirmed a good generalization capability of the proposed network.

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Periodical:

Advanced Materials Research (Volume 510)

Pages:

723-728

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.510.723

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Online since:

April 2012

Authors:

Liang Cheng, Hui Chang, Bin Tang, Hong Chao Kou, Jin Shan Li

Keywords:

Artificial Neutral Network, Flow Stress, Genetic Algorithm (GA), High-Nb TiAl Alloys, Hot Deformation

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References

[1] G.L. Chen, J.G. Wang, X.D. Ni, J.P. Lin, Y.L. Wang: Intermetallics. Vol. 13 (2005), pp.329-336.

Google Scholar

[2] Zhang Y G, Chen G L, Guo JT, Wan X J, Fend D: Structural Intermetallics[M]. Beijing: National Defense Industry Press, (2001).

Google Scholar

[3] M. Yamaguchi, et al: MRS Vol. 6 (1995), p.364.

Google Scholar

[4] Helmut Clemens, Heinrich Kestler: Advanced Engineering Materials. Vol. 243 (2000), pp.46-65.

Google Scholar

[5] Xiaoming He, Zhongqi Yu, Xinmin Lai: Materials Letters. Vol. 62 (2008), pp.4181-4183.

Google Scholar

[6] X.J. Xu, J.P. Lin , Y.L. Wang, Z. Lin, G.L. Chen: Materials Science and Engineering A Vol. 416 (2006), p.98–103.

Google Scholar

[7] J.P. Lin, X.J. Xu, Y.L. Wang, S.F. He, Y. Zhang, X.P. Song, G.L. Chen: Intermetallics Vol. 15 (2007), pp.668-674.

Google Scholar

[8] B. Liu, Y. Liua, W. Zhang, J.S. Huang: Intermetallics Vol. 13 (2010), pp.1-6.

Google Scholar

[9] Tu JV: J Clin Epidemiol Vol. 50 (1997), pp.1309-1310.

Google Scholar

[10] R. Ebrahimi, A. Najafizadeh: Journal of Materials Processing Technology Vol. 152 (2004), p.136–143.

Google Scholar

[11] Y.C. Lin a, Jun Zhang, Jue Zhong: Computational Materials Science Vol. 43 (2008), p.752–758.

Google Scholar

[12] D Whitley, T Starkweather, C Bogart: Parallel Computing. Vol. 14 (1990), p.34.

Google Scholar