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Effects of aging parameters on hardness and electrical conductivity of Cu-Cr-Sn-Zn alloy by artificial neural network

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Abstract

In order to predict and control the properties of Cu-Cr-Sn-Zn alloy, a model of aging processes via an artificial neural network (ANN) method to map the non-linear relationship between parameters of aging process and the hardness and electrical conductivity properties of the Cu-Cr-Sn-Zn alloy was set up. The results show that the ANN model is a very useful and accurate tool for the property analysis and prediction of aging Cu-Cr-Sn-Zn alloy. Aged at 470–510 °C for 4-1 h, the optimal combinations of hardness 110–117 (HV) and electrical conductivity 40.6–37.7 S/m are available respectively.

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Authors and Affiliations

  1. College of Materials Science and Engineering, Henan University of Science and Technology, Luoyang, 471003, China

    Juan-hua Su  (苏娟华), Shu-guo Jia  (贾淑果) & Feng-zhang Ren  (任凤章)

Authors
  1. Juan-hua Su  (苏娟华)
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  2. Shu-guo Jia  (贾淑果)
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  3. Feng-zhang Ren  (任凤章)
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Corresponding author

Correspondence to Juan-hua Su  (苏娟华).

Additional information

Foundation item: Project(2006AA03Z528) supported by the National High-Tech Research and Development Program of China; Project(102102210174) supported by the Science and Technology Research Project of Henan Province, China; Project(2008ZDYY005) supported by Special Fund for Important Forepart Research in Henan University of Science and Technology

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Su, Jh., Jia, Sg. & Ren, Fz. Effects of aging parameters on hardness and electrical conductivity of Cu-Cr-Sn-Zn alloy by artificial neural network. J. Cent. South Univ. Technol. 17, 715–719 (2010). https://doi.org/10.1007/s11771-010-0545-x

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  • DOI: https://doi.org/10.1007/s11771-010-0545-x

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