Modeling of rapidly solidified aging process of Cu-Cr-Sn-Zn alloy by an artificial neural network

Juan Hua Su; He Jun Li; Qi Ming Dong; Ping Liu; Bao Hong Tian

doi:10.1016/j.commatsci.2004.12.064

Modeling of rapidly solidified aging process of Cu-Cr-Sn-Zn alloy by an artificial neural network

Juan Hua Su, He Jun Li, Qi Ming Dong, Ping Liu, Bao Hong Tian

School of Materials Sience and Engineering

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

This paper uses an artificial neural network (ANN) and Levenberg-Marquardt training algorithm to model the non-linear relationship between parameters of rapidly solidified aging processes and mechanical and electrical properties of Cu-Cr-Sn-Zn alloy. The predicted values of the ANN are in accordance with the testing data. A basic repository on the domain knowledge of rapidly solidified age processes is established. Rapidly solidified aging processes can greatly enhance the hardness and electrical conductivity for Cu-Cr-Sn-Zn alloy. At 500 °C for 15 min aging the hardness and conductivity can reach 170 HV and 64% IACS respectively.

Original language	English
Pages (from-to)	151-156
Number of pages	6
Journal	Computational Materials Science
Volume	34
Issue number	2
DOIs	https://doi.org/10.1016/j.commatsci.2004.12.064
State	Published - Sep 2005

Keywords

Aging
Artificial neural network
Cu-Cr-Sn-Zn alloy
Levenberg-Marquard algorithm
Rapid solidification

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10.1016/j.commatsci.2004.12.064

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title = "Modeling of rapidly solidified aging process of Cu-Cr-Sn-Zn alloy by an artificial neural network",

abstract = "This paper uses an artificial neural network (ANN) and Levenberg-Marquardt training algorithm to model the non-linear relationship between parameters of rapidly solidified aging processes and mechanical and electrical properties of Cu-Cr-Sn-Zn alloy. The predicted values of the ANN are in accordance with the testing data. A basic repository on the domain knowledge of rapidly solidified age processes is established. Rapidly solidified aging processes can greatly enhance the hardness and electrical conductivity for Cu-Cr-Sn-Zn alloy. At 500 °C for 15 min aging the hardness and conductivity can reach 170 HV and 64% IACS respectively.",

keywords = "Aging, Artificial neural network, Cu-Cr-Sn-Zn alloy, Levenberg-Marquard algorithm, Rapid solidification",

author = "Su, {Juan Hua} and Li, {He Jun} and Dong, {Qi Ming} and Ping Liu and Tian, {Bao Hong}",

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AU - Su, Juan Hua

AU - Li, He Jun

AU - Dong, Qi Ming

AU - Liu, Ping

AU - Tian, Bao Hong

PY - 2005/9

Y1 - 2005/9

N2 - This paper uses an artificial neural network (ANN) and Levenberg-Marquardt training algorithm to model the non-linear relationship between parameters of rapidly solidified aging processes and mechanical and electrical properties of Cu-Cr-Sn-Zn alloy. The predicted values of the ANN are in accordance with the testing data. A basic repository on the domain knowledge of rapidly solidified age processes is established. Rapidly solidified aging processes can greatly enhance the hardness and electrical conductivity for Cu-Cr-Sn-Zn alloy. At 500 °C for 15 min aging the hardness and conductivity can reach 170 HV and 64% IACS respectively.

AB - This paper uses an artificial neural network (ANN) and Levenberg-Marquardt training algorithm to model the non-linear relationship between parameters of rapidly solidified aging processes and mechanical and electrical properties of Cu-Cr-Sn-Zn alloy. The predicted values of the ANN are in accordance with the testing data. A basic repository on the domain knowledge of rapidly solidified age processes is established. Rapidly solidified aging processes can greatly enhance the hardness and electrical conductivity for Cu-Cr-Sn-Zn alloy. At 500 °C for 15 min aging the hardness and conductivity can reach 170 HV and 64% IACS respectively.

KW - Aging

KW - Artificial neural network

KW - Cu-Cr-Sn-Zn alloy

KW - Levenberg-Marquard algorithm

KW - Rapid solidification

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M3 - 文章

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SN - 0927-0256

VL - 34

SP - 151

EP - 156

JO - Computational Materials Science

JF - Computational Materials Science

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ER -

Modeling of rapidly solidified aging process of Cu-Cr-Sn-Zn alloy by an artificial neural network

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