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science China(Technological sciences) 2005年第5期48卷 510-520页

作者： su juanhua1,{1.,{2., xi’an 710072. China 2. college of materials science and engineering, Henan university of science and Technology, Luoyang 471.03, China 1. college of {1. {2. and engineering Northwestern Polytechnical University 710072 Xi’an China 2. {1. of materials {2. and engineering Henan University of Science and Technology 471003 Luoyang China

The thermomechanical treatment process is effective in enhancing the properties of the lead frame copper alloy. In this study, an optimal pattern of the thermomechanical processes for Cu-Cr-Zr was investegated using an intelligent control technique consisting of neural networks and genetic algorithms. The input parameters of the artificial neural network (ANN) are the reduction ratio of cold rolling, aging temperature and aging time. The outputs of the ANN model are the two most important properties of hardness and conductivity. Based on the successfully trained ANN model, genetic algorithms (GA) are used to optimize the input parameters of the model and select perfect combinations of thermomechanical processing parameters and properties. The good generalization performance and optimized results of the integrated model are achieved.

关键词： Cu-Cr-Zr alloy, thermomechanical processing optimization, artificial neural network, genetic algorithms.

Effect of Fe Particle on the surface Pee{1.ng in Cu-Fe-P Lead Frame

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Journal of Wuhan university of Technology({1. {2.) 2006年第3期21卷 18-20页

作者：苏娟华 college of {1. {2. and engineering Henan University of Science and Technology Luoyang 471003ChinaCollege of Materials Science and Engineering Northwestern Polytechnical University Xi'an 710072China

Under the surface pee{1.ng of Cu- Fe- P lead frame alloy larger Fe particles were observed by energy dispersive spectroscopy. By using the large strain two-dinension plane strain model and elastic plastic finite element method, the cause for pee{1.ng damage of Cu-Fe-P lead frame aUoy was investigated. The results show that when the content of Fe particles is more than 30% at local Fe-rich area the intense stress coacentration in the Fe particle would make the Fe particle broken up. The high equivalent stress mutation and the mismatch of equivalent strain 1.% at the two sides of intefrace make it easy to develop the crack and pee{1.ng damage on finish rol{1.ng. The larger Fe particles in the Cu-Fe-P alloy should be avoided.

关键词： Cu-Fe-P lead frame surface pee{1.ng equivalent Stress equivalent strain

Microstructure and Properties of Cu-Cr-Zr Alloy after Rapidly Solidified Aging and Solid Solution Aging

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Journal of materials {1. & Technology 2005年第4期21卷 475-478页

作者： ping {1. {2. su qiming dong hejun li college of {1. {2. and engineering Henan University of Science and Technology Luoyang 471003 China college of {1. {2. and engineering Northwestern Polytechnical University Xi'an 710072 China

The structure and properties of Cu-Cr-Zr alloy were studied after rapidly solidified aging and solid solution *** the early stage of aging （500℃ for 1. rain）, the hardness and the conductivity of the alloy rapidly solidified are 1.3 HV and 72. IACS, respectively. Under the same aging condition, the hardness and electrical conductivity of the alloy solid solution treated can reach 86 HV and 47% IACS, respectively. The microstructure was analyzed, and the grain size after rapid solidification is much smaller than that after solid solution treatment. By rapidly solidified aging the fine precipitates distribute inside the grains and along the grain boundary, while by solid solution aging there are large Cr particles along the grain boundary.

关键词： Cu-Cr-Zr alloy Properties Solid solution aging Rapidly solidified aging

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基于人工神经网络的铜合金时效性能研究

特种铸造及有色合金 2003年第5期23卷 29-31页

作者：苏娟华李贺军董企铭刘平康布熙西北工业大学河南科技大学

利用神经网络对Cu Cr Zr合金时效温度、时间与硬度和电导率样本集进行学习 ,采用改进的BP网络算法———Levenberg Marquardt算法 ,建立了时效强化工艺BP神经网络模型。预测结果表明 :该BP神经网络可以充分挖掘样本蕴含的领域知识 ,可... 详细信息

利用神经网络对Cu Cr Zr合金时效温度、时间与硬度和电导率样本集进行学习 ,采用改进的BP网络算法———Levenberg Marquardt算法 ,建立了时效强化工艺BP神经网络模型。预测结果表明 :该BP神经网络可以充分挖掘样本蕴含的领域知识 ,可以对材料性能进行有效预测和分析。

关键词： Cu-Cr-Zr合金时效强化铜合金人工神经网络 Levenberg-Marquardt算法