Deep learning in sheet-bulk metal forming part design
Sauer C, Schleich B, Wartzack S (2018)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2018
Pages Range: 2999 - 3010
Conference Proceedings Title: DS92: Proceedings of the DESIGN 2018 15th International Design Conference
Event location: Dubrovnik
URI: https://www.archiv.mfk.tf.fau.de/?file=pubmfk_5b0bee1e19eb7
Abstract
Within the Transregional Collaborative Research Centre 73, a self-learning engineering workbench is
being developed. It assists product developers in designing sheet-bulk metal formed (SBMF) parts by
computing the effects of given product and process characteristics on the product properties. This
contribution presents a novel approach to using deep learning methods for the properties prediction. By
making use of a parameter study of 20 SBMF part designs, a metamodel is trained and used to predict
the total equivalent plastic strain on local level as an indicator for part manufacturability.
Authors with CRIS profile
Christopher Sauer
Sonderforschungsbereich/Transregio 73 Umformtechnische Herstellung von komplexen Funktionsbauteilen mit Nebenformelementen aus Feinblechen - Blechmassivumformung
Benjamin Schleich
Chair of Engineering Design
Sandro Wartzack
Chair of Engineering Design
Related research project(s)
How to cite
APA:
Sauer, C., Schleich, B., & Wartzack, S. (2018). Deep learning in sheet-bulk metal forming part design. In DS92: Proceedings of the DESIGN 2018 15th International Design Conference (pp. 2999 - 3010). Dubrovnik, HR.
MLA:
Sauer, Christopher, Benjamin Schleich, and Sandro Wartzack. "Deep learning in sheet-bulk metal forming part design." Proceedings of the DESIGN 2018 - 15th Internaional Design Conference, Dubrovnik 2018. 2999 - 3010.
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