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In addition to research papers, the Design Society is developing several valuable resources for those interested in the study of design. These include a repository of PhD theses, a library of case studies and transcripts of design activities, and an archive of our newsletters. Please note that these resources are accessible exclusively to Design Society members.

DEEP LEARNING IN SHEET-BULK METAL FORMING PART DESIGN

Sauer, Christopher; Schleich, Benjamin; Wartzack, Sandro

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Type:
Papers
DS 92: Proceedings of the DESIGN 2018 15th International Design Conference
Year:
2018
Editor:
Marjanović D., Štorga M., Škec S., Bojčetić N., Pavković N.
Author:
Sauer, Christopher; Schleich, Benjamin; Wartzack, Sandro
Series:
DESIGN
Section:
SYSTEMS ENGINEERING AND DESIGN
Page(s):
2999-3010
DOI number:
https://doi.org/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.
Keywords:
data mining, data driven design, characteristics and properties, deep learning
DESIGN 2018 - 15th International Design Conference
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