DETECTION OF SURFACE DEFECTS IN FRICTION STIR WELDED JOINTS BY USING A NOVEL MACHINE LEARNING APPROACH
The Friction stir welding process is a new entrant in welding technology. The FSW joints have high strength and helps in weight saving considerably than the other joining process as no filler material is added during welding. The weld quality is affected because of various kinds of defects occurring...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
The Association of Intellectuals for the development of Science in Serbia - "The Serbian Academic Center"
2020-03-01
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| Series: | Applied Engineering Letters |
| Subjects: | |
| Online Access: | https://www.aeletters.com/wp-content/uploads/2020/03/AEL00182.pdf |
| Summary: | The Friction stir welding process is a new entrant in welding technology. The FSW joints have high strength and helps in weight saving considerably than the other joining process as no filler material is added during welding. The weld quality is affected because of various kinds of defects occurring during the FSW process. Defects like cavity, surface grooves and flash could
occur due to inappropriate set of process parameters which results in excessive or insufficient heat input.
Defects analysis can be done by several non-destructive methods like immersion ultrasonic techniques, X-ray radiography, thermography, eddy current testing, synchrotron technique etc. In the present work the image
processing techniques are applied over the test samples to detect the surface defects like pin holes, surface grooves etc. |
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| ISSN: | 2466-4677 2466-4847 |