Process analysis and optimization for failure energy of spot welded titanium alloy

被引:22
作者
Zhao, Dawei [1 ,2 ]
Wang, Yuanxun [1 ]
Wang, Xiaodong [2 ]
Wang, Xuenong [2 ]
Chen, Fa [2 ]
Liang, Dongjie [3 ]
机构
[1] Huazhong Univ Sci & Technol, Dept Mech, Sch Civil Engn & Mech, Wuhan 430074, Peoples R China
[2] Xinjiang Acad Agr Sci, Inst Agr Mechanizat, Urumqi 830091, Peoples R China
[3] Guangxi Zhuang Autonomous Reg Inst Metrol & Test, Nanjing 530007, Jiangsu, Peoples R China
关键词
Small scale resistance spot welding; Response surface methodology; Failure energy; Regression model; Titanium alloy; Artificial fish swarm algorithm optimization; RESPONSE-SURFACE METHODOLOGY; WELDING PARAMETERS; ALUMINUM-ALLOY; TAGUCHI METHOD; STEEL; PERFORMANCE; ATTRIBUTES; PREDICTION; BEHAVIOR; DESIGN;
D O I
10.1016/j.matdes.2014.03.070
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Titanium and its alloys have been applied in many industrial fields because of their high specific strength, good corrosion resistance and high thermal stability. Whereas, there is limited valuable references for recommendations of welding parameter selection and specific standards of the small scale resistance spot welding (SSRSW) of titanium alloy though it has been applied in many industrial production fields. Seventeen tests were designed according to the three-level three-factor Box-Behnken experimental design and the mathematical model correlating the process parameters and the failure energy was established on the basis of response surface methodology (RSM) technique. And then this model was used to analyze the effects/interactions of the welding parameters on the failure energy. The verification test results which were conducted with completely new welding parameters verified that the model presented in this paper was effective and robust. Sensitivity analysis was also carried out to explore the impact of each process parameter on the quality of welding joint. The optimal combination of process parameters for maximum failure energy of the welded joint was obtained using the model based on artificial fish swarm algorithm (AFSA). (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:479 / 489
页数:11
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