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Temperature-based quality analysis in ultrasonic welding of copper sheets with microstructural joint evaluation and machine learning methods

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Abstract

Ultrasonic metal welding (USMW) is a highly attractive joining technology due to high energy efficiency and solid-state joint formation. Various joining solutions for conductor materials can be realized with USMW. Still, a big challenge for complex industrial applications is an adequate process monitoring that allows to cope with inevitable and complex process fluctuations. In this work, the suitability of contactless temperature measurements for process monitoring of copper sheet welding is examined and compared with the suitability of vibration measurements by means of machine learning methods. Different sensor signals acquired during welding on a metrological test rig are used for predicting the tensile shear strength of the joint. Results show that quality predictions based on temperatures exceed the state-of-the-art monitoring based on the welding energy. Yet, solely temperature-based predictions are exceeded by quality predictions based on either welding machine signals or tool vibration measurements. To further explore temperature-based quality analysis, joint microstructure analyses are carried out. These reveal concurring joint formation mechanisms associated with the mechanical and thermal process domains. To finally cover both domains in quality prediction, a sensor-fusion-based regression model is set up relying on vibration and temperature measurements. This fusion model exceeds all previously considered regression models with a mean absolute percentage error of 7.4% on the test data set. These results stress the importance of both process domains and suggest the combination of temperature and vibration measurements as a good starting point for future industrial monitoring of USMW processes.

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Acknowledgements

Ingo Kesel and Tobias Beck are acknowledged for help with microstructural investigations and for fruitful discussions.

Funding

This research project received funding by the German Federal Ministry for Economic Affairs and Energy (BMWi) within the funding programme ‘Elektro-Mobil’ (FKZ 01MV18003C / EProFIL) according to a decision of the German Federal Parliament. The authors are responsible for the contents of this publication.

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Authors and Affiliations

  1. Bosch Research, Robert Bosch GmbH, Renningen, Germany

    Elisabeth Birgit Schwarz, Fabian Bleier & Friedhelm Guenter

  2. Production Technology Group, Technische Universitaet Ilmenau, Ilmenau, Germany

    Elisabeth Birgit Schwarz & Jean Pierre Bergmann

  3. Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany

    Ralf Mikut

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  1. Elisabeth Birgit Schwarz
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Correspondence to Elisabeth Birgit Schwarz.

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Author contribution

E.B. Schwarz — conceptualization, methodology, investigation, software, validation, writing — original draft, writing — review and editing. F. Bleier — conceptualization, writing — review and editing. F. Guenter — conceptualization, writing — review and editing. R. Mikut — conceptualization, writing — review and editing. J.P. Bergmann — conceptualization, writing — review and editing, supervision. All authors have approved the manuscript.

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Recommended for publication by Commission IX - Behaviour of Metals Subjected to Welding

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Schwarz, E.B., Bleier, F., Guenter, F. et al. Temperature-based quality analysis in ultrasonic welding of copper sheets with microstructural joint evaluation and machine learning methods. Weld World 67, 1437–1448 (2023). https://doi.org/10.1007/s40194-023-01463-0

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