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Semi-solid Processing of Steel-Al-7 Graphite Binding Plate 被引量:2
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作者 张鹏 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS 2002年第3期46-49,共4页
The bonding of steel plate to Al 7graphite slurry was studied for the first time. The relationship model about preheat temperature of steel plate,solid fraction of Al 7graphite slurry, rolling speed and interfacial ... The bonding of steel plate to Al 7graphite slurry was studied for the first time. The relationship model about preheat temperature of steel plate,solid fraction of Al 7graphite slurry, rolling speed and interfacial shear strength of bonding plate could be established by artificial neural networks perfectly.This model could be optimized with a genetic algorithm.The optimum bonding parameters are:516℃ for preheat temperature of steel plate,32.5% for solid fraction of Al 7graphite slurry and 12mm/s for rolling speed,and the largest interfacial shear strength of bonding plate is 70.6MPa. 展开更多
关键词 bonding of steel plate to Al 7graphite slurry artificial neural networks genetic algorithm
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The Bonding Properties of Solid Steel to Liquid Aluminum
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作者 张鹏 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS 2005年第1期25-29,共5页
The bonding of solid steel plate to liquid al uminum was studied using rapid solidification. The relationship models of interf acial shear strength and thickness of interfacial layer of bonding plate vs bond ing para... The bonding of solid steel plate to liquid al uminum was studied using rapid solidification. The relationship models of interf acial shear strength and thickness of interfacial layer of bonding plate vs bond ing parameters (such as preheat temperature of steel plate, temperature of alumi num liquid and bonding time) were respectively established by artificial neural networks perfectly.The bonding parameters for the largest interfacial shear stre ngth were optimized with genetic algorithm successfully. They are 226℃ for preh eating temperature of steel plate, 723℃ for temperature of aluminum liquid and 15.8s for bonding time, and the largest interfacial shear strength of bonding pl ate is 71.6 MPa . Under these conditions, the corresponding reasonable thickne ss of interfacial layer (10.8μm) is gotten using the relationship model establi shed by artificial neural networks. 展开更多
关键词 bonding of steel plate to liquid aluminum rapid solidification artificial neural networks genetic algorithm
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Thickness of compound layer in steel-aluminum solid to liquid bonding
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作者 PengZhang YunhuiDu +4 位作者 HanwuLiu ShumingXing DabenZeng JianzhongCui LiminBa 《Journal of University of Science and Technology Beijing》 CSCD 2003年第5期48-52,共5页
The bonding of solid steel plate to liquid aluminum was studied using rapidsolidification. The surface of solid steel plate was defatted, descaled, immersed (in K_2ZrF_6 fluxaqueous solution) and stoved. In order to d... The bonding of solid steel plate to liquid aluminum was studied using rapidsolidification. The surface of solid steel plate was defatted, descaled, immersed (in K_2ZrF_6 fluxaqueous solution) and stoved. In order to determine the thickness of Fe-Al compound layer at theinterface of steel-aluminum solid to liquid bonding under rapid solidification, the interface ofbonding plate was investigated by SEM (Scanning Electron Microscope) experiment. The relationshipbetween bonding parameters (such as preheat temperature of steel plate, temperature of aluminumliquid and bonding time) and thickness of Fe-Al compound layer at the interface was established byartificial neural networks (ANN) perfectly. The maximum of relative error between the output and thedesired output of the ANN is only 5.4%. From the bonding parameters for the largest interfacialshear strength of bonding plate (226℃ for preheat temperature of steel plate, 723℃ for temperatureof aluminum liquid and 15.8 s for bonding time), the reasonable thickness of Fe-Al compound layer10.8 μm was got. 展开更多
关键词 bonding of steel plate to liquid aluminum rapid solidification thickness ofFe-Al compound layer artificial neural networks
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