东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2019, Vol. 40 ›› Issue (3): 370-375.DOI: 10.12068/j.issn.1005-3026.2019.03.013

• 机械工程 • 上一篇    下一篇

基于Kriging-PSO智能算法优化焊接工艺参数

马小英1， 孙志礼1， 张毅博1， 臧旭2   

  1. (1. 东北大学 机械工程与自动化学院， 辽宁 沈阳110819; 2. 中国人民解放军驻沈阳飞机工业(集团)有限公司， 辽宁 沈阳110850)
  • 收稿日期:2017-12-27 修回日期:2017-12-27 出版日期:2019-03-15 发布日期:2019-03-08
  • 通讯作者: 马小英
  • 作者简介:马小英(1982-)，女，甘肃兰州人，东北大学博士研究生; 孙志礼(1957-)，男，山东巨野人，东北大学教授，博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(51775097).

Optimization of Welding Process Parameters Based on Kriging-PSO Intelligent Algorithm

MA Xiao-ying1， SUN Zhi-li1， ZHANG Yi-bo1， ZANG Xu2   

  1. 1. School of Mechanical Engineering & Automation， Northeastern University， Shenyang 110819， China; 2. People′s Liberation Army in Shenyang Aircraft Industry
  • Received:2017-12-27 Revised:2017-12-27 Online:2019-03-15 Published:2019-03-08
  • Contact: MA Xiao-ying
  • About author:-
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摘要： 焊接工艺参数是影响焊接成型质量的关键因素.由于工艺参数和焊接接头的力学性能之间的关系是多维隐式的，因此，提出了一种Kriging模型和粒子群相结合的优化算法，解决了在交流钨极氩弧焊中3.5mm厚镁合金薄板的工艺参数优化问题.首先通过田口正交法构建样本集，其次建立输出和输入之间的Kriging代理模型，并通过提出的算法获得最优工艺参数组合及其力学性能.结果表明:通过该算法获得的最优工艺参数组合，其对应的焊接接头的抗拉强度、屈服强度和平均显微硬度分别达到母材的97.6%，98%和91.5%，减少了经济和时间成本，提高了焊接工艺设计能力.

关键词: 交流钨极氩弧焊, 镁合金, 焊接工艺参数, Kriging模型, 粒子群优化

Abstract: Welding process parameters are the key factors affecting the quality of welding. Since the relationship between process parameters and the mechanical properties of welded joints is multi-dimensional and implicit， an optimization algorithm combining Kriging model and particle swarm optimization is proposed to optimize the process parameters of 3.5mm magnesium alloy sheet in AC_TIG welding. Firstly， the sample set is constructed by Taguchi orthogonal method. Secondly， the Kriging surrogate model is established between output and input， and then the optimal combination of process parameters and its mechanical properties are obtained by the proposed algorithm. The results show that such optimal process parameters as tensile strength， yield strength and average micro-hardness of the welded joints reach 97.6%， 98% and 91.5% of the base metal respectively. The proposed algorithm not only reduces economic and time costs， but also improves the welding process design capabilities.

Key words: AC_TIG welding, magnesium alloy, welding process parameters, Kriging model, particle swarm optimization

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