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

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

Publikováno v: Bock, F E, Paulsen, T, Brkovic, N, Rieckmann, L, Kroeger, D, Wolgast, D, Zander, P, Suhuddin, U F H, dos Santos, J F & Klusemann, B 2021, 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 ., 2589, ESAFORM 2021-24th International Conference on Material Forming, ULiège Library, Liège, 24th International Conference on Material Forming-ESAFORM 2021, Liège, Belgium, 14.04.21 . https://doi.org/10.25518/esaform21.2589

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://www.mendeley.com/catalogue/d7b822c9-0728-3378-b54f-d7fd4e4f7e41/