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YU Huiping, YUAN Yue, HAN Changlu, LI Xiaoyang. Analysis of test about residual stress of super steel spot welding under different process[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2016, 37(9): 35-38.
Citation: YU Huiping, YUAN Yue, HAN Changlu, LI Xiaoyang. Analysis of test about residual stress of super steel spot welding under different process[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2016, 37(9): 35-38.

Analysis of test about residual stress of super steel spot welding under different process

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  • Received Date: October 21, 2014
  • In order to analyze the effect on resistance spot welding residual stress of super steel under different process, the B1500HS steel sheet which is after the quenching process of the fast cooling is choosed as the material in this experiment. The resistance spot welding component is conducted by German spot welding robot NIMAK. The X-ray diffraction method is used to measure the components residual stress of weld nugget, heat affected zone and parent metal area. The results showing that the residual stress is distributing symmetrically around the welding core and the value of residual stress increases first then decreases from welding core to the parent metal. Besides, the residual stress reaches its maximum value in the heat affected zone and characterized by tensile stress, then it reduced to be pressure stress away from the welding core, meanwhile the maximum tensile stress significantly powerful than the maximum pressure stress. The electrode diameter can increase the residual stress with the increase of welding current the residual stress increasise first than decrease. The forging pressure can effectively reduces the value of residual stress.
  • 韩长录. 超高强钢点焊残余应力的测试与数值分析[D].北京:北京工业大学,2014.
    常保华, 都东, 岁波, 等. 锻压力对铝合金点焊接头疲劳行为的影响[J]. 焊接学报, 2005, 26(8): 5-8. Chang Baohua, Du Dong, Sui Bo, et al. Effect of forging force on fatigue behavior of spot-welded joints of alum iniu-m alloy[J]. Transactions of the China Welding Instruction, 2005, 26(8): 5-8.
    魏强, 宋建岭, 苏再为, 等. 不等厚异种铝合金点焊焊核偏移工艺研究[J]. 焊接, 2014(6):63-65. Wei Qiang, Song Jianling, Su Zaiwei, et al. The Research of ranging from heterogeneous thick aluminum alloy spot welding nuclear migration process[J].Welding & Joining, 2014(6):63-65.
    孙芳芳. 铝合金电焊过程的数值模拟[D].合肥: 合肥工业大学, 2010.
    宇慧平, 王伟伟, 刘跃华, 等. 基于响应面法的超高强钢点焊结构的尺寸优化[J]. 焊接学报, 2014, 35(4): 45-48. Yu Huiping, Wang Weiwei, Liu Yuehua, et al. Dimensions optimization of ultra-high strength steel spot weld based on response surface methodology[J]. Transations of the China Welding Institution, 2014, 35(4): 45-48.
    邓黎鹏, 刘金合, 柯黎明, 等. 400 MPa级细晶粒钢电阻点焊接头组织分析[J]. 焊接学报, 2012, 33(6): 85-88. Deng Lipeng, Liu Jinhe, Ke Liming, et al. Pilot study of resistance spot welding technology for 400 MPa ultrafine grained steel[J]. Transations of the China Welding Institution, 2012, 33(6): 85-88.
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