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<p>Manufacturers are looking intently for more rapid Friction Stir Welding (FSW), speeds in a manufacturing environment to improve quantity. The variable regions increase as welding speed increases, potentially leading to an increase in the number of defects. Consequently, it’s essential to use an effective inspection method that uses non-destructive evaluation (NDE) to identify the surface defect origins. In the current proposal, a novel microwave-based non-destructive evaluation is utilized, employing an open-ended rectangular waveguide probe to detect defects in samples processed via FSW. The “Skin effect” approach is employed at microwave frequencies (8–12&#xa0;GHz) for identifying potential external defects in welded samples. Further research indicated that the suggested interpolation image processing technique accurately identifies defects present within welded samples. This proposed microwave non-destructive evaluation model successfully predicted defects with a minimum deviation of 1&#xa0;mm.</p>

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Identification of Defect in Friction Stir Welding Processed Material Using Microwave Non-Destructive Evaluation and Image Processing

  • 期刊论文

  • 发表日期：

    2025年7月1日

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  • Arunkumar Anbalagan,
  • Vimal Samsingh Ramalingam,
  • Esther Florence Sundarsingh,
  • B. Sakthi Abirami,
  • P. V. Shravan

摘要

Manufacturers are looking intently for more rapid Friction Stir Welding (FSW), speeds in a manufacturing environment to improve quantity. The variable regions increase as welding speed increases, potentially leading to an increase in the number of defects. Consequently, it’s essential to use an effective inspection method that uses non-destructive evaluation (NDE) to identify the surface defect origins. In the current proposal, a novel microwave-based non-destructive evaluation is utilized, employing an open-ended rectangular waveguide probe to detect defects in samples processed via FSW. The “Skin effect” approach is employed at microwave frequencies (8–12 GHz) for identifying potential external defects in welded samples. Further research indicated that the suggested interpolation image processing technique accurately identifies defects present within welded samples. This proposed microwave non-destructive evaluation model successfully predicted defects with a minimum deviation of 1 mm.