2022
DOI: 10.1016/j.scriptamat.2022.114765
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Force data-driven machine learning for defects in friction stir welding

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Cited by 29 publications
(5 citation statements)
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“…Due to the two tools worked on the specimen surfaces, the one-step double-side FSW has more complex forces than forces on the one-side FSW. These forces significantly affect the joint quality, including defects and mechanical properties of welding joints [39,40]. These forces should be directed to channel the energy produced into the workpiece joining process.…”
Section: Apparatus Design On One-step Double-side Fswmentioning
confidence: 99%
“…Due to the two tools worked on the specimen surfaces, the one-step double-side FSW has more complex forces than forces on the one-side FSW. These forces significantly affect the joint quality, including defects and mechanical properties of welding joints [39,40]. These forces should be directed to channel the energy produced into the workpiece joining process.…”
Section: Apparatus Design On One-step Double-side Fswmentioning
confidence: 99%
“…At this condition, severe tool wear is observed after the welding experiment is finished, especially the probe threads and three flats. It has been reported that severe probe wear results in an insufficient material flow around the rotating tool during the welding process, and a high compression force between the tool and the workpiece due to material jam at the retreating side leads to a high F y [24].…”
Section: Welding Force Characteristicsmentioning
confidence: 99%
“…In line with improving predictive models, Verma et al [18] also tackled the prediction of tensile behavior in FS-welded AA7039 using ML, reinforcing the critical role of ML in achieving predictive accuracy that can anticipate the tensile outcomes of welded materials. The study by Guan et al [19] suggests a direction toward force data-driven ML for identifying defects in FSW, an application that may revolutionize quality assurance by automating the detection of FSW flaws.…”
Section: Introductionmentioning
confidence: 99%