Modeling of aging processes in Cu-Cr-Sn-Zn alloy by an artificial neural network

Juan Hua Su; He Jun Li

Modeling of aging processes in Cu-Cr-Sn-Zn alloy by an artificial neural network

Juan Hua Su
, He Jun Li

材料学院

Northwestern Polytechnical University Xian

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

A model of the non-linear relationship between parameters of age processes and hardness and conductivity properties of Cu-Cr-Sn-Zn alloy is established by the artificial neural network (ANN). The improved model is developed by the Levenberg-Marquardt training algorithm. The results show that the ANN model has good generalization performance and makes the predicting and analyzing the properties of Cu-Cr-Sn-Zn alloy available.

源语言	英语
页（从-至）	286-288
页数	3
期刊	Jinshu Rechuli/Heat Treatment of Metals
卷	30
期	SUPPL.
出版状态	已出版 - 9月 2005

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title = "Modeling of aging processes in Cu-Cr-Sn-Zn alloy by an artificial neural network",

abstract = "A model of the non-linear relationship between parameters of age processes and hardness and conductivity properties of Cu-Cr-Sn-Zn alloy is established by the artificial neural network (ANN). The improved model is developed by the Levenberg-Marquardt training algorithm. The results show that the ANN model has good generalization performance and makes the predicting and analyzing the properties of Cu-Cr-Sn-Zn alloy available.",

keywords = "Aging processes, Conductivity, Cu-Cr-Sn-Zn alloy, Hardness, Neural Network",

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AU - Su, Juan Hua

AU - Li, He Jun

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N2 - A model of the non-linear relationship between parameters of age processes and hardness and conductivity properties of Cu-Cr-Sn-Zn alloy is established by the artificial neural network (ANN). The improved model is developed by the Levenberg-Marquardt training algorithm. The results show that the ANN model has good generalization performance and makes the predicting and analyzing the properties of Cu-Cr-Sn-Zn alloy available.

AB - A model of the non-linear relationship between parameters of age processes and hardness and conductivity properties of Cu-Cr-Sn-Zn alloy is established by the artificial neural network (ANN). The improved model is developed by the Levenberg-Marquardt training algorithm. The results show that the ANN model has good generalization performance and makes the predicting and analyzing the properties of Cu-Cr-Sn-Zn alloy available.

KW - Aging processes

KW - Conductivity

KW - Cu-Cr-Sn-Zn alloy

KW - Hardness

KW - Neural Network

UR - https://www.scopus.com/pages/publications/26644474784

M3 - 文章

AN - SCOPUS:26644474784

SN - 0254-6051

VL - 30

SP - 286

EP - 288

JO - Jinshu Rechuli/Heat Treatment of Metals

JF - Jinshu Rechuli/Heat Treatment of Metals

IS - SUPPL.

ER -

Modeling of aging processes in Cu-Cr-Sn-Zn alloy by an artificial neural network

摘要

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