Work

Deep Learning in Sheet-bulk Metal Forming Part Design

Year: 2018

Type: conference-paper

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.

Source: Proceedings of the ... International Design Conference/Design ...

DOI: 10.21278/idc.2018.0147

Authors: Christopher Sauer, Benjamin Schleich, Sandro Wartzack

Language: English

Topic: Metal Forming Simulation Techniques

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

Funder: Deutsche Forschungsgemeinschaft

Awards: G2198776063, G6673089398