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Schulze, Julian (2020) Application of AI-based methods for the evaluation of a joining process for multi-material joints. Masterarbeit, Hochschule Albstadt-Sigmaringen.
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Kurzfassung
For the automotive industry, lightweight concepts become more and more important due to economic and ecologic constraints, therefore often multi-material structures are used to counteract this problem. An innovative joining solution for such structures is the flow drill screwing process. With increasing digitalization, promising technologies like artificial intelligence are implemented in manufacturing to increase productivity and gain competitive advantage. In this work, the application of different machine learning models for flow drill screwing joints is investigated, with focus on giving an overview about different methods and transparency of these. The strategy is first to generate a dataset. For this purpose, the used materials steel and aluminium are characterized by a literature review and with mechanical tests. Afterwards, single-lap-shear joints are generated using the flow drill screwing process. The joints are further investigated regarding their failure behaviour under shear tension. Selected mechanical and geometrical values of the materials are defined as input variables and the maximum force of the resulting joint is defined as the output/target variable. Finally, different machine learning models and data preparation techniques are proposed to find the most promising model. The results give an overview of the performance of different models based on the used data preparation strategies. The artificial neural network shows the best performance for this dataset. This model is then fine-tuned and evaluated regarding the effect of the training set size on the performance. Further methods concerning explainability and structure of the model are shown.
| elib-URL des Eintrags: | https://elib.dlr.de/140573/ | ||||||||
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| Dokumentart: | Hochschulschrift (Masterarbeit) | ||||||||
| Titel: | Application of AI-based methods for the evaluation of a joining process for multi-material joints | ||||||||
| Autoren: |
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| Datum: | 15 Dezember 2020 | ||||||||
| Referierte Publikation: | Ja | ||||||||
| Open Access: | Nein | ||||||||
| Status: | veröffentlicht | ||||||||
| Stichwörter: | Flow drill screwing, multi-material design, artificial intelligence, machine learning, regression, crisp-dm | ||||||||
| Institution: | Hochschule Albstadt-Sigmaringen | ||||||||
| HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||
| HGF - Programm: | Verkehr | ||||||||
| HGF - Programmthema: | Verkehrssystem | ||||||||
| DLR - Schwerpunkt: | Verkehr | ||||||||
| DLR - Forschungsgebiet: | V VS - Verkehrssystem | ||||||||
| DLR - Teilgebiet (Projekt, Vorhaben): | V - Energie und Verkehr (alt) | ||||||||
| Standort: | Stuttgart | ||||||||
| Institute & Einrichtungen: | Institut für Fahrzeugkonzepte > Werkstoff- und Verfahrensanwendungen Gesamtfahrzeug | ||||||||
| Hinterlegt von: | Greß, Alexander | ||||||||
| Hinterlegt am: | 29 Apr 2021 16:52 | ||||||||
| Letzte Änderung: | 29 Apr 2021 16:52 |
Verfügbare Versionen dieses Eintrags
- Application of AI-based methods for the evaluation of a joining process for multi-material joints. (deposited 29 Apr 2021 16:52) [Gegenwärtig angezeigt]
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