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作 者:PengZHANG YunhuiDU HanwuLIU DabenZENG JianzhongCUI LiminBA
机构地区:[1]DepartmentofMechanicalEngineering,NorthernJiaotongUniversity,Beijing100044,China [2]DepartmentofMechanicalEngineering,TsinghuaUniversity,Beijing100084,China [3]DepartmentofMetalForming,NortheastenUniversity,Shenyang110004,China [4]AnshanAutomobileFittingsFactory,Anshan114014,China
出 处:《Journal of Materials Science & Technology》2005年第2期265-268,共4页材料科学技术(英文版)
基 金:This project was supported by the National Natural Science Foundation of China(No.50274047 and 50304001);the Bejing Jiaotong University Foundation.
摘 要:The pressing bonding of steel plate with QTi3.5-3.5graphite slurry was studied. The relationship among preheating temperature of steel plate, preheating temperature of dies, solid fraction of QTi3.5-3.5graphite slurry, and interfacial shear strength of bonding plate could be established with artificial neural networks perfectly. This model could be optimized with a genetic algorithm. The results show that the optimum bonding parameters are: 618℃ for preheating temperature of steel plate, 526℃ for preheating temperature of dies and 46.2% for solid fraction of QTi3.5-3.5graphite slurry, and the largest interfacial shear strength of bonding plate is 128.3 MPa.
关 键 词:Steel-mushy QTi3.5-3.5graphite bonding Artificial neural networks Genetic algorithm
分 类 号:TG3[金属学及工艺—金属压力加工]
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