The Network Prediction of Hot Temperature Flow Stress and Dynamic Recrystallization of AZ61 Alloy

Quan Li; Wen Jun Liu; Ren Ju Cheng; Cheng Li; Shan Jiang; Su Qin Luo; Xian Quan Jiang; Fu Sheng Pan

doi:10.4028/www.scientific.net/AMR.989-994.544

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994The Network Prediction of Hot Temperature Flow...

The Network Prediction of Hot Temperature Flow Stress and Dynamic Recrystallization of AZ61 Alloy

Abstract:

In this paper, the Back-Propagation neural network (BP network) and the establishment of the AZ61 magnesium alloy high temperature constitutive model and test data obtained for training the neural network, after training the neural network to become a knowledge-based constitutive model formed AZ61 magnesium alloy flow stress and dynamic recrystallization of the neural network model tested by the neural network model with traditional regression methods predict contrast, results showed that the higher the accuracy of the neural network model for dealing with a large number of test data to establish the constitutive relations of materials with high stress and promotion of value.

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Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

544-547

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.544

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Online since:

July 2014

Authors:

Quan Li*, Wen Jun Liu, Ren Ju Cheng, Cheng Li, Shan Jiang, Su Qin Luo, Xian Quan Jiang, Fu Sheng Pan

Keywords:

Flow Stress, Magnesium Alloy, Network, Prediction

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References

[1] NIU JI-tai, SUM LEI-Jian, LI HAI-tao. research micro-alloy hot-rolled austenitic grain size model based on artificial neural networks, Materials Science and Technology, 1997, 7 (1) 12-16.

Google Scholar

[2] XIA SHAN-mu, Li DA-yong. the mechanical properties of cast iron for fast prediction of artificial neural networks, Applied Sciences, 2002, 20 (3) : 309-312.

Google Scholar

[3] DAI QI-Xun, DAI XI-Min. Journal of Neural Network in austenitic steel design Steel Research, 1997, 9 (6) : 37-40.

Google Scholar

[4] LIU XUE-feng, WANG LING-yun, artificial neural network model aluminum-lithium alloyhigh temperature deformation flow stress of Chongqing University (Natural Science Edition), 2001, 24 (2).

Google Scholar

[5] P.D. Hodgson, D. Mcfarlane R.K. Gibbs. The Mathematical Modeling of Hot Rolling. Journal of Japan society for Technology of Plasticity, 1987, (28): 195－197.

Google Scholar

[6] Oh. Si, Wu WT, Tang J P, et al., Capabilities and Applications of FEM Code Deform . Journal of Materials Processing Technology, 1991, 27: 25－42.

DOI: 10.1016/0924-0136(91)90042-d

Google Scholar