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 HR

URI: https://www.archiv.mfk.tf.fau.de/?file=pubmfk_5b0bee1e19eb7

DOI: 10.21278/idc.2018.0147

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.

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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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