Advanced Search
XU Fei, CHEN Li, LU Wei, GUO Luyun. Effect of heat input on weld appearance for fiber laser welding 6A02 aluminum alloy[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2017, 38(8): 119-123. DOI: 10.12073/j.hjxb.20150830004
Citation: XU Fei, CHEN Li, LU Wei, GUO Luyun. Effect of heat input on weld appearance for fiber laser welding 6A02 aluminum alloy[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2017, 38(8): 119-123. DOI: 10.12073/j.hjxb.20150830004

Effect of heat input on weld appearance for fiber laser welding 6A02 aluminum alloy

More Information
  • Received Date: August 29, 2015
  • The fiber laser beam with high power density is used for welding 6A02 aluminum alloy with 1.0mm thickness. And the effects of heat input on weld macrography, structure and properties are studied. The results show that the stable fully penetration weld could be obtained when the heat input is controlled in the range of 8-12 J/mm and the welding speed is very high. The typical crosssection of the welds always presents near X shape. The characteristic of the welds could reduce the nonuniformity of welding temperature field. It also could reduce welding buckling and deformation. The columnar microstructure is formed near the fusion line. The mixed microstructures, including columnar grains and equiaxed grains, distribute in the center of the weld. The transition from columnar microstructure to mixed microstructures can be found from the fusion line to the weld center. With the heat input reduced, the microstructures of the weld zone are tendency to fine, the soften phenomenon near the fusion line is helpful to weak, the microhardness of the weld zone and the tensile strength of the joints are increasing slightly.
  • 左铁钏, 肖荣诗, 陈 铠, 等. 高强铝合金的激光加工[M]. 北京: 国防工业出版社, 2002.[2] 陈 俐, 巩水利. 铝合金激光焊接技术的应用与发展[J]. 航空制造技术, 2011(11): 46-49. Chen Li, Gong Shuili. Application and development of laser welding technology for aluminum alloy[J]. Aeronautical Manufacturing Technology, 2011(11): 46-49.[3] Quintino L, Costa A, Miranda R,etal. Welding with high power fiber lasers-a preliminary study[J]. Materials and Design, 2007, 28(4): 1231-1237.[4] 陈 俐. 航空钛合金激光全熔透稳定性及焊接物理冶金研究[D]. 武汉: 华中科技大学, 2005.[5] 姚 伟, 巩水利, 陈 俐. 钛合金激光穿透焊的焊缝成形(Ⅱ)[J]. 焊接学报, 2004, 25(5): 74-76. Yao Wei, Gong Shuili, Chen Li. Weld shaping for laser fully penetration welding titanium alloy(Ⅱ)[J]. Transactions of the China Welding Institution, 2004, 25(5): 74-76.[6] 许 飞, 巩水利, 陈 俐, 等. 钛合金光纤激光焊接接头特征分析[J]. 航空制造技术, 2013, 436(16): 90-92. Xu Fei, Gong Shuili, Chen Li,etal. Characteristics of titanium alloy by fibre laser welding[J]. Welding Technology in Aerospace Industry, 2013, 436(16): 90-92.[7] 许 飞, 杨 璟, 巩水利, 等. 热输入对铝合金光纤激光穿透焊缝成形的影响[J]. 中国激光, 2014, 41(12): 1203001. Xu Fei, Yang Jing, Gong Shuili,etal. Effect of heat input on weld appearance for fiber laser beam full penetration welding aluminum alloy[J]. Chinese Journal of Lasers, 2014, 41(12): 1203001.[8] 中国航空材料手册编辑委员会. 中国航空材料手册第3卷, 铝合金 镁合金[M]. 第2版. 北京: 中国标准出版社, 2002,[9] 温 鹏, 张旭东, 陈武柱, 等. 薄板激光焊时失稳变形及其控制[J]. 焊接学报, 2006, 27(9): 99-102. Wen Peng, Zhang Xudong, Chen Wuzhu,etal. Buckling distortion of laser welded thin plates and its control by dynamic cooling[J]. Transactions of the China Welding Institution, 2006, 27(9): 99-102.[10] 闫俊霞, 霍立兴, 张玉凤, 等. 焊接薄板失稳变形预测方法[J]. 焊接学报, 2005, 26(6): 50-53. Yan Junxia, Huo Lixing, Zhang Yufeng,etal. Prediction method of buckling distortion of welding thin plate[J]. Transactions of the China Welding Institution, 2005, 26(6): 50-53.[11] Chon L T, Han M S. Thermal and mechanical evolution of welding induced buckling distortion[J]. Journal of the Chinese Institute of Engineers, 2004, 27(6): 905-918.[12] Michaleris P, Debiccari A. Prediction of welding distortion[J]. Welding Journal, 1997, 76(4): 172-181.[13] 陈伯蠡. 焊接冶金原理[M]. 北京: 清华大学出版社, 1991.[14] 周振丰. 焊接冶金学(金属焊接性)[M]. 北京: 机械工业出版社, 1995.
  • Related Articles

    [1]HONG Yuxiang, YING Qiluo, LIN Kai, WANG Kaiming, WANG Yaoqi. Arc welding molten pool image recognition based on attention mechanism and transfer learning[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2025, 46(4): 94-102. DOI: 10.12073/j.hjxb.20240112003
    [2]LI Chengwen, JI Haibiao, YAN Zhaohui, LIU Zhihong, MA Jianguo, WANG Rui, WU Jiefeng. Prediction of residual stress and deformation of 316L multi-layer multi-pass welding based on GA-BP neural network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(5): 20-28. DOI: 10.12073/j.hjxb.20230520002
    [3]CHEN Chen, ZHOU Fangzheng, LI Chenglong, LIU Xinfeng, JIA Chuanbao, XU Yao. Prediction method of plasma arc welding molten pool melting state based on spatial and channel characteristics[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2023, 44(4): 30-38. DOI: 10.12073/j.hjxb.20220516001
    [4]GAO Changlin, SONG Yanli, ZUO Hongzhou, ZHANG Cheng. Cause diagnosis of welding defects based on adaptive PSO-BP neural network with dynamic weighting[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(1): 98-106. DOI: 10.12073/j.hjxb.20210515001
    [5]FAN Ding, HU Ande, HUANG Jiankang, XU Zhenya, XU Xu. X-ray image defect recognition method for pipe weld based on improved convolutional neural network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2020, 41(1): 7-11. DOI: 10.12073/j.hjxb.20190703002
    [6]CHEN Yuquan, GAO Xiangdong. Neural network compensation for micro-gap weld detection by magneto-optical imaging[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2016, 37(10): 33-36.
    [7]CHEN Zhenhua, SHI Yaowu, ZHAO Haiyan. Ultrasonic testing of spot weld based on spectrum analysis and artificial neural network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2009, (10): 76-80.
    [8]DI Xinjie, LI Wushen, BAI Shiwu, LIU Fangming. Metal magnetic memory signal recognition by neural network for welding crack[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2008, (3): 13-16.
    [9]SHI Yu, FAN Ding, CHEN Jian-hong. Predication of properties of welded joints based on neural network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2004, (2): 73-76.
    [10]PENG Pai, WU Lin, TIAN JIN-Song, WANG Xue-feng, FENG Ying-jun. Application of Neural Networks in Welding Parameters's Planning of Robots[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2001, (4): 39-42.
  • Cited by

    Periodical cited type(7)

    1. 高东,李永利,邓颖,周好斌. 旁路耦合电弧TIG焊原理及工艺研究. 热加工工艺. 2025(01): 65-69 .
    2. 孟美情,韩俭,朱瀚钊,梁哲滔,蔡养川,张欣,田银宝. 基于多丝电弧增材制造研究现状. 材料工程. 2025(05): 46-62 .
    3. 王梦真,万占东,林健. 电弧增材制造工艺及数值仿真研究进展. 大型铸锻件. 2024(01): 7-12 .
    4. 李博洋,巴现礼,陈帅帅,徐国敏,刘黎明. 不同路径下的低碳钢三丝间接电弧增材制造组织与性能. 焊接技术. 2024(10): 1-6+145 .
    5. 张加恒,黄祎,郭顺,杨东青,闫德俊,李东,王克鸿. 超音频MIG辅助三丝电弧增材制造工艺研究. 电焊机. 2023(02): 104-110 .
    6. 吴涛,谭振,王立伟,梁志敏,汪殿龙. 异质双丝间接电弧增材制造Al-Mg-Cu合金组织与力学性能. 焊接学报. 2023(10): 64-70+136 . 本站查看
    7. 朱强,姚屏,许斯帆,许可昱. 316L不锈钢电弧增材制造工艺研究. 精密成形工程. 2023(11): 164-170 .

    Other cited types(1)

Catalog

    Article views (929) PDF downloads (623) Cited by(8)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return