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Volumetric Defect Detection in Friction Stir Welding Through Convolutional Neural Networks Generalized Across Multiple Aluminum-Alloys and Sheet Thicknesses

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3rd International Conference on Advanced Joining Processes 2023 (AJP 2023)

Part of the book series: Proceedings in Engineering Mechanics ((PEM))

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Abstract

Friction Stir Welding (FSW) is a solid-state welding process, which has strongly impacted welding technology, particularly for aluminum alloy applications. Due to its high-quality welds in all aluminum alloys, comparatively low specific heat input at high energy efficiency and ecological friendliness, FSW is used in a rapidly growing number of safety critical applications. Currently destructive and non-destructive testing methods are added as a separate process step to verify weld seam quality, adding complexity, cost, and time to the production. Imperfections are detected late in the production process and require costly rework or discarding of the assembly. Several studies have shown the possibility of using Deep Neural Networks to evaluated data recorded during the FSW process. Analyzed data includes thermal measurements, acoustic measurements, image or video data and most commonly the comparably large and distinctive process feedback forces. This study is a continuation of efforts by the research group. In this study Convolutional Neural Networks (CNN) based on the DenseNet architecture were successfully trained to classify FSW process force recordings supplemented with weld meta-data to detect volumetric subsurface defects. The data-sets were generated while welding different aluminum alloys in multiple sheet thicknesses over a wide range of spindle rotational speeds and feedrates. The networks classification accuracy as well as the ability to generalize across the different welded aluminum alloys, sheet thicknesses and corresponding welding tools was evaluated. Achieving a classification accuracy of 98.37%, the development aims to provide a reliable and cost-effective quality monitoring solution with a wide range of applicability to replace the required expensive and time intensive ultrasonic, x-ray or macro-section weld seam testing.

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Acknowledgements

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—EXC 2023 Internet of Production—390621612.

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Authors and Affiliations

  1. Welding and Joining Institute (ISF), RWTH Aachen University, Pontstraße 49, 52062, Aachen, Germany

    Pascal Rabe, Alexander Schiebahn & Uwe Reisgen

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  1. Pascal Rabe
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  2. Alexander Schiebahn
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  3. Uwe Reisgen
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Correspondence to Pascal Rabe .

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  1. Faculty of Engineering, University of Porto, Porto, Portugal

    Lucas F. M. da Silva

  2. University of Lisbon, IDMEC, Instituto Superior Técnico, Lisbon, Portugal

    Paulo Martins

  3. Welding and Joining Institute, RWTH Aachen University, Aachen, Nordrhein-Westfalen, Germany

    Uwe Reisgen

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Rabe, P., Schiebahn, A., Reisgen, U. (2024). Volumetric Defect Detection in Friction Stir Welding Through Convolutional Neural Networks Generalized Across Multiple Aluminum-Alloys and Sheet Thicknesses. In: da Silva, L.F.M., Martins, P., Reisgen, U. (eds) 3rd International Conference on Advanced Joining Processes 2023. AJP 2023. Proceedings in Engineering Mechanics. Springer, Cham. https://doi.org/10.1007/978-3-031-54732-4_4

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  • DOI: https://doi.org/10.1007/978-3-031-54732-4_4

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