DS 80-4 Proceedings of the 20th International Conference on Engineering Design (ICED 15) Vol 4: Design for X, Design to X, Milan, Italy, 27-30.07.15

Year: 2015
Editor: Christian Weber, Stephan Husung, Marco Cantamessa, Gaetano Cascini, Dorian Marjanovic, Serena Graziosi
Author: Breitsprecher, Thilo; Sauer, Christopher; Sperber, Christian; Wartzack, Sandro
Series: ICED
Institution: Friedrich-Alexander-Universitaet Erlangen-Nuernberg (FAU), Germany
Section: Design for X, Design to X
Page(s): 183-192
ISBN: 978-1-904670-67-4
ISSN: 2220-4334


Sheet-bulk metal forming (SBMF) is an emerging and sustainable manufacturing technology that offers potential both for shortening process chains and for designing new geometry features that enable functional integration. In order to make use of the latter one the design engineers need design-relevant and manufacturing related knowledge that has to be acquired at early stages of the process development process. This objective is pursued by our self-learning engineering assistance system (SLASSY) that supports the knowledge-based analysis of sheet-bulk metal formed parts. It does so by means of metamodels that have been derived from manufacturing data via our KDD-based selflearning process. In this paper we present the foundations for a knowledge-based synthesis of such parts. That is, SLASSY will be enabled to automatically propose a design-for-manufacture geometry. We discuss the idea of design-for-manufacture from the SBMF point of view and show why our objective calls for multi-objective optimization and which algorithms meet our requests. Finally, a use case shows the utilization of evolutionary algorithms.

Keywords: Design For X, Computational Design Synthesis, Knowledge Management, Optimisation

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