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HAN Tao, GU Shiwei, XU Liang, ZHANG Hongjie, OUYANG Kai. Study on stress and deformation of K-TIG welded joint[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(11): 125-132. DOI: 10.12073/j.hjxb.2019400299
Citation: HAN Tao, GU Shiwei, XU Liang, ZHANG Hongjie, OUYANG Kai. Study on stress and deformation of K-TIG welded joint[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(11): 125-132. DOI: 10.12073/j.hjxb.2019400299
shu

Study on stress and deformation of K-TIG welded joint

  • 1.

    China University of Petroleum (East China), Qingdao 266580, China

  • 2.

    Harbin Modern Welding Technology Co., Ltd., Harbin 150028, China

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  • Received Date: January 01, 2019
    Graphical Abstract
    • Abstract
  • Keyhole gas tungsten arc welding (K-TIG) process of Q345 low alloy steel plates was simulated by SYSWELD software. The temperature field of K-TIG welding process was simulated with three different combined heat sources, and compared with actual weld profile obtained by experiment. It was found that the temperature field obtained by the combination of double ellipsoid heat source on the upper half and 3D Gaussian heat source on the lower half was similar to the actual situation. The effects of plate thickness, gap and welding speed on the deformation and stress of K-TIG welded joints were investigated by K-TIG welding numerical simulation. The results indicate that reducing the thickness of weld plates is beneficial to reducing Z-direction deformation and transverse residual stress, leaving appropriate gap is beneficial to reducing residual stress, increasing welding speed is beneficial to reducing deformation after welding, but is not beneficial to controlling residual stress after welding.
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    • References (11)
  • Lathabai S, Jarvis B L, Barton K J. Comparison of keyhole and conventional gas tungsten arc welds in commercially pure titanium[J]. Materials Science & Engineering A, 2001, 299(1-2):81-93.
    Jarvis B L, Ahmed N U. Development of keyhole mode gas tungsten arc welding process[J]. Science & Technology of Welding & Joining, 2000, 5(1):21-28.
    樊文飞, 罗震, 冯悦峤, 等. 低合金钢Q345的深熔TIG焊研究[J]. 上海交通大学学报, 2016(s1):102-105 Fan Wenfei, Luo Zhen, Feng Yue'an, et al. Study on deep penetration TIG welding of low alloy steel Q345[J]. Journal of Shanghai Jiaotong University, 2016(s1):102-105
    Feng Y, Luo Z, Liu Z, et al. Keyhole gas tungsten arc welding of AISI 316L stainless steel[J]. Materials & Design, 2015, 85:24-31.
    Cui S L, Liu Z M, Fang Y X, et al. Keyhole process in K-TIG welding on 4 mm thick 304 stainless steel[J]. Journal of Materials Processing Technology, 2017, 243:217-228.
    Fang Y X, Liu Z M, Cui S L, et al. Improving Q345 weld microstructure and mechanical properties with high frequency current arc in keyhole mode TIG welding[J]. Journal of Materials Processing Technology, 2017, 250:280-288.
    张瑞华, 栗海霞, 李明, 等. K-TIG焊接电弧特性的数值分析[J]. 电焊机, 2012, 42(12):7-11 Zhang Ruihua, Li Haixia, Li Ming, et al. Numerical analysis of arc characteristics of K-TIG welding[J]. Electric Welding Machine, 2012, 42(12):7-11
    冯悦峤. 中厚钢板的深熔TIG焊工艺研究及温度场数值模拟[D]. 天津:天津大学, 2016.
    黄逸飞, 罗震, 敖三三, 等. 基于非对称热源的异种钢深熔TIG焊接数值模拟[J]. 机械工程学报, 2018, 54(2):41-47 Huang Yifei, Luo Zhen, Ao Sansan, et al. Numerical simulation of deep penetration TIG welding of dissimilar steels based on asymmetric heat source[J]. Journal of Mechanical Engineering, 2018, 54(2):41-47
    赵欣, 张彦华. 焊接过程温度场数值模拟中热源模型的选择[C]//中国机械工程学会. 全国计算机在焊接中的应用学术与技术交流会, 2008:429-433.
    胡庆贤. 穿孔等离子弧焊接温度场的有限元分析[D]. 济南:山东大学, 2007.
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