Machine-learning-assisted prediction of the mechanical properties of Cu-Al alloy-Reference-Cited by-同舟云学术

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Machine-learning-assisted prediction of the mechanical properties of Cu-Al alloy

Published:2020-03 Issue:3 Volume:27 Page:362-373
ISSN:1674-4799
Container-title:International Journal of Minerals, Metallurgy and Materials
language:en
Short-container-title:Int J Miner Metall Mater

Author:

Deng Zheng-hua,Yin Hai-qing,Jiang Xue,Zhang Cong,Zhang Guo-fei,Xu Bin,Yang Guo-qiang,Zhang Tong,Wu Mao,Qu Xuan-hui

Publisher

Springer Science and Business Media LLC

Subject

Materials Chemistry,Metals and Alloys,Geochemistry and Petrology,Mechanical Engineering,Mechanics of Materials

Link

http://link.springer.com/content/pdf/10.1007/s12613-019-1894-6.pdf

Reference51 articles.

1. J.L. Liu, H.Y. Huang, and J.X. Xie, Effects of aging treatment on the microstructure and superelasticity of columnar-grained Cu71Al18Mn11 shape memory alloy, Int. J. Miner. Metall. Mater., 23(2016), No. 10, p. 1157.

2. M. Izadinia and K. Dehghani, Microstructural evolution and mechanical properties of nanostructured Cu-Al-Ni shape memory alloys, Int. J. Miner. Metall. Mater., 19(2012), No. 4, p. 333.

3. A. Sata and B. Ravi, Comparison of some neural network and multivariate regression for predicting mechanical properties of investment casting, J. Mater. Eng. Perform., 23(2014), No. 8, p. 2953.

4. N. Altinkok and R. Koker, Neural network approach to prediction of bending strength and hardening behaviour of particulate reinforced (Al—Si—Mg)—aluminium matrix composites, Mater. Des., 25(2004), No. 7, p. 595.

5. B. Zhao, T.Y. Yu, W.F. Ding, X.Y. Li, and H.H. Su, BP neural network based flexural strength prediction of open-porous Cu—Sn—Ti composites, Prog. Nat. Sci., 28(2018), No. 3, p. 315.

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