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材料工程 >

2022 , Vol. 50 >Issue 8: 45 - 59

DOI: https://doi.org/10.11868/j.issn.1001-4381.2021.000633

铝合金专栏

铝合金搅拌摩擦焊超声检测研究进展

  • 金士杰 , * ,
  • 田鑫 ,
  • 林莉 , *
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  • 大连理工大学 无损检测研究所, 辽宁 大连 116024
金士杰(1984—)，男，副教授，博士，研究方向：材料无损检测与评价，联系地址：辽宁省大连市高新园区凌工路2号大连理工大学无损检测研究所(116024)，E-mail：
林莉(1970—)，女，教授，博士，研究方向：材料无损检测与评价，联系地址：辽宁省大连市高新园区凌工路2号大连理工大学无损检测研究所(116024)，E-mail：

收稿日期: 2021-07-09

  网络出版日期: 2022-08-16

基金资助

国家重点研发项目(2019YFA0709003)

大连市科技创新基金项目(2020JJ26GX041)

中央高校基本科研业务费(DUT20ZD204)

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Research progress in ultrasonic testing for friction stir welding of aluminum alloy

  • Shijie JIN , * ,
  • Xin TIAN ,
  • Li LIN , *
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  • NDT & E Laboratory, Dalian University of Technology, Dalian 116024, Liaoning, China

Received date: 2021-07-09

  Online published: 2022-08-16

Copyright

All rights reserved, without authorization
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摘要

铝合金搅拌摩擦焊(friction stir welding, FSW)焊接参数选择不当将会产生隧道孔、未焊透(lack of penetration, LOP)和吻接等取向复杂、细微紧贴的缺陷。首先, 本文简述了FSW焊缝与典型缺陷特征, 总结了超声检测时面临纵向分辨力低、缺陷表征不完整、材料与缺陷声阻抗接近和灵敏度不足等难点。随后, 从常规超声、超声衍射时差法(time-of-flight diffraction, TOFD)、相控阵超声检测技术和其他超声检测技术等方面综述了现有的铝合金FSW超声检测研究工作。最后, 结合超声信号处理方法和机器学习方法对研究前景进行展望: 可以通过分析和提取信号特征, 进一步提升超声检测分辨力和信噪比, 并实现取向复杂缺陷和细微紧贴缺陷的精准辨识与定量。

关键词： 搅拌摩擦焊; 铝合金; 超声检测; 缺陷

本文引用格式

金士杰 , 田鑫 , 林莉 . 铝合金搅拌摩擦焊超声检测研究进展[J]. 材料工程, 2022 , 50(8) : 45 -59 . DOI: 10.11868/j.issn.1001-4381.2021.000633

Abstract

The defects in friction stir welding (FSW) of aluminum alloy, e.g., tunnel defect, lack of penetration (LOP) and kissing bond, are with complex shape and narrow gap by inappropriate welding parameters. Firstly, the characteristics of FSW welding and typical defects were briefly described, and the difficult points in ultrasonic testing were summarized from the low time resolution, incomplete characterization of irregular defects, close acoustic impedance between kissing bond and Al alloy and the reduction of detection sensitivity. Subsequently, the research work on the ultrasonic testing for FSW of aluminum alloy were reviewed from the following four aspects, including conventional ultrasonic testing, time-of-flight diffraction (TOFD), phased array ultrasonic testing and other ultrasonic testing techniques. Finally, the research on the ultrasonic signal processing methods and machine learning methods was prospected. The signal characteristics were analyzed and extracted to further improve the resolution and signal-to-noise ratio of ultrasonic testing and realize the accurate identification and quantification for complex shape defects and subtle defects.

Key words： friction stir welding; aluminum alloy; ultrasonic testing; defect

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