Aging properties prediction of the lead frame Cu-Cr-Sn-Zn alloy via neural network

He Jun Li; Juan Hua Su; Qi Ming Dong; Ping Liu; Feng Zhang Ren

doi:10.4028/0-87849-960-1.3331

Aging properties prediction of the lead frame Cu-Cr-Sn-Zn alloy via neural network

He Jun Li, Juan Hua Su, Qi Ming Dong, Ping Liu, Feng Zhang Ren

School of Materials Sience and Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

The aging process of lead frame Cu-Cr-Sn-Zn alloy has only been studied empirically by trial-and-error method so far. This paper builds up the prediction model of the aging properties via a supervised artificial neural network(ANN) to model the non-linear relationship between parameters of aging process with respect to hardness and electrical conductivity properties of the alloy. The improved model is developed by the Levenberg- Marquardt training algorithm. The predicted values of the ANN coincide with the tested data. So the ANN system is effective and successful for predicting and analyzing the properties of Cu-Cr-Sn-Zn alloy. The optimized processing parameters are available at 475°C -520°C aging for 2h-1h.

Original language	English
Pages (from-to)	3331-3334
Number of pages	4
Journal	Materials Science Forum
Volume	475-479
Issue number	IV
DOIs	https://doi.org/10.4028/0-87849-960-1.3331
State	Published - 2005
Event	PRICM 5: The Fifth Pacific Rim International Conference on Advanced Materials and Processing - Beijing, China Duration: 2 Nov 2004 → 5 Nov 2004

Keywords

Aging Process
Artificial Neural Network
Cu-Cr-Sn-Zn Alloy
Lead Frame

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10.4028/0-87849-960-1.3331

Cite this

@article{418cdb90e4d14390bd102cd2623e775e,

title = "Aging properties prediction of the lead frame Cu-Cr-Sn-Zn alloy via neural network",

abstract = "The aging process of lead frame Cu-Cr-Sn-Zn alloy has only been studied empirically by trial-and-error method so far. This paper builds up the prediction model of the aging properties via a supervised artificial neural network(ANN) to model the non-linear relationship between parameters of aging process with respect to hardness and electrical conductivity properties of the alloy. The improved model is developed by the Levenberg- Marquardt training algorithm. The predicted values of the ANN coincide with the tested data. So the ANN system is effective and successful for predicting and analyzing the properties of Cu-Cr-Sn-Zn alloy. The optimized processing parameters are available at 475°C -520°C aging for 2h-1h.",

keywords = "Aging Process, Artificial Neural Network, Cu-Cr-Sn-Zn Alloy, Lead Frame",

author = "Li, {He Jun} and Su, {Juan Hua} and Dong, {Qi Ming} and Ping Liu and Ren, {Feng Zhang}",

year = "2005",

doi = "10.4028/0-87849-960-1.3331",

language = "英语",

volume = "475-479",

pages = "3331--3334",

journal = "Materials Science Forum",

issn = "0255-5476",

publisher = "Trans Tech Publications Ltd",

number = "IV",

note = "PRICM 5: The Fifth Pacific Rim International Conference on Advanced Materials and Processing ; Conference date: 02-11-2004 Through 05-11-2004",

}

TY - JOUR

T1 - Aging properties prediction of the lead frame Cu-Cr-Sn-Zn alloy via neural network

AU - Li, He Jun

AU - Su, Juan Hua

AU - Dong, Qi Ming

AU - Liu, Ping

AU - Ren, Feng Zhang

PY - 2005

Y1 - 2005

N2 - The aging process of lead frame Cu-Cr-Sn-Zn alloy has only been studied empirically by trial-and-error method so far. This paper builds up the prediction model of the aging properties via a supervised artificial neural network(ANN) to model the non-linear relationship between parameters of aging process with respect to hardness and electrical conductivity properties of the alloy. The improved model is developed by the Levenberg- Marquardt training algorithm. The predicted values of the ANN coincide with the tested data. So the ANN system is effective and successful for predicting and analyzing the properties of Cu-Cr-Sn-Zn alloy. The optimized processing parameters are available at 475°C -520°C aging for 2h-1h.

AB - The aging process of lead frame Cu-Cr-Sn-Zn alloy has only been studied empirically by trial-and-error method so far. This paper builds up the prediction model of the aging properties via a supervised artificial neural network(ANN) to model the non-linear relationship between parameters of aging process with respect to hardness and electrical conductivity properties of the alloy. The improved model is developed by the Levenberg- Marquardt training algorithm. The predicted values of the ANN coincide with the tested data. So the ANN system is effective and successful for predicting and analyzing the properties of Cu-Cr-Sn-Zn alloy. The optimized processing parameters are available at 475°C -520°C aging for 2h-1h.

KW - Aging Process

KW - Artificial Neural Network

KW - Cu-Cr-Sn-Zn Alloy

KW - Lead Frame

UR - http://www.scopus.com/inward/record.url?scp=17044362296&partnerID=8YFLogxK

U2 - 10.4028/0-87849-960-1.3331

DO - 10.4028/0-87849-960-1.3331

M3 - 会议文章

AN - SCOPUS:17044362296

SN - 0255-5476

VL - 475-479

SP - 3331

EP - 3334

JO - Materials Science Forum

JF - Materials Science Forum

IS - IV

T2 - PRICM 5: The Fifth Pacific Rim International Conference on Advanced Materials and Processing

Y2 - 2 November 2004 through 5 November 2004

ER -

Aging properties prediction of the lead frame Cu-Cr-Sn-Zn alloy via neural network

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Keywords

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