Evaluation of mechanical property predictions of refill Friction Stir Spot Welding joints via machine learning regression analyses on DoE data

Autor: Benjamin Klusemann, Lennart Rieckmann, Nikola Brkovic, Dominik Wolgast, U.F.H. Suhuddin, Frederic E. Bock, Tino Paulsen, Jorge Fernandez dos Santos, Dennis Kroeger, Philip Zander

Publikováno v: Bock, F.; Paulsen, T.; Brkovic, N.; Rieckmann, L.; Kroeger, D.; Wolgast, D.; Zander, P.; Suhuddin, U.; dos Santos, J.; Klusemann, B.: Evaluation of mechanical property predictions of refill Friction Stir Spot Welding joints via machine learning regression analyses on DoE data. In: ESAFORM 2021-24th International Conference on Material Forming. Virtual, 14.04.2021-16.04.2021, 2021. 2589. (DOI: /10.25518/esaform21.2589) (ISBN: 978-287019302-0)

The high-potential of lightweight components consisting of similar or dissimilar materials can be exploited by Solid-State Joining techniques. Whereas defects such as pores and hot cracking are often an issue in fusion-based joining processes, via so

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https://publications.hzg.de/id/39622