Deep learning in sheet-bulk metal forming part design

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Sauer C, Schleich B, Wartzack S
Publication year: 2018
Conference Proceedings Title: DS92: Proceedings of the DESIGN 2018 15th International Design Conference
Pages range: 2999 - 3010
Language: English


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.


FAU Authors / FAU Editors

Sauer, Christopher
Sonderforschungsbereich/Transregio 73 Umformtechnische Herstellung von komplexen Funktionsbauteilen mit Nebenformelementen aus Feinblechen - Blechmassivumformung
Schleich, Benjamin Dr.-Ing.
Lehrstuhl für Konstruktionstechnik
Wartzack, Sandro Prof. Dr.-Ing.
Lehrstuhl für Konstruktionstechnik


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.

BibTeX: 

Last updated on 2019-08-01 at 09:10