Constitutive Relationship Model of Al-W Alloy Using Artificial Neural Network

Article Preview

Abstract:

Isothermal compression experiment of Al-W alloy was carried out using Gleeble1500 thermodynamic simulator within a temperature range of 420°C-570°C and a strain rate range of 0.001s-1-1s-1。And the constitutive relationship model for this alloy was successfully developed using BP neural network. In the proposed model, the input variables are strain, strain rate and deformation temperature while the flow stress is the output variable. The results show that absolute maximum error between predicted and experimental values of flow stress is less than 10.0Mpa, the correlation coefficient is 0.993. It was found that the established constitutive relationship model could provide a good representation of the test data and describe the whole deforming process better compared with the traditional method.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 1004-1005)

Pages:

1120-1124

Citation:

Online since:

August 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] QI Jia zhong, CHEN Pei jing, CHEN Li min, et al. CHINA METALLURGY, 2005, 15(9): 4-8.

Google Scholar

[2] ZHEN Xueping, LI Ping, QU Xuanhui, et al. Rare Metals Letters, 2005, 24(3): 6-9.

Google Scholar

[3] LIU Yong, HUANG Bai-yun, LONG Zheng-yi, et al. Materials Science and Engineering of Powder Metallurgy, 2005, 10(1): 10-20.

Google Scholar

[4] BHADESHIA H K D H, DIMITRIUL R C, FORSIKL S, PAK J H, RYU J H. Mater Sci Technol, 2009, 25: 504-510.

Google Scholar

[5] LIN Qiquan, LI Xiaolong, ZHU Yuanzhi, et al. Natural Science Journal of Xiangtan University, 2004, 26(3): 112-115.

Google Scholar

[6] LIU Fang, SHAN De-bin, LV Yan, YANG Yu-ying. Journal of Harbin Institute of Technology, 2004, 11(4): 368-371.

Google Scholar

[7] WANG Yu, SUN Zhi-chao, LI Zhi-ying, et al. The Chinese Journal of Nonferrous Metals, 2011, 21(11): 2880-2887.

Google Scholar

[8] ZURADA J M. Introduction to artificial neural networks. New York: West Publishing Co, (1992).

Google Scholar

[9] SELLARS C M, MCTEGART W J. Acta Metall, 1966, 14: 1136-1138.

Google Scholar