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KNOWLEDGE-DRIVEN DESIGN FOR ADDITIVE MANUFACTURING: A FRAMEWORK FOR DESIGN ADAPTATION

Published online by Cambridge University Press:  19 June 2023

Paul Schaechtl ,
Stefan Goetz ,
Benjamin Schleich  and
Sandro Wartzack
Paul Schaechtl*
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg;
Stefan Goetz
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg;
Benjamin Schleich
Affiliation:
Technische Universität Darmstadt
Sandro Wartzack
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg;
*
Schaechtl, Paul, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, schaechtl@mfk.fau.de

Abstract

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Due to the high freedom of design, additive manufacturing (AM) is increasingly substituting conventional manufacturing technology in several sectors. However, the knowledge and the awareness for the suitable design of additively manufactured components or assemblies ensuring manufacturability and fully realizing its potential is still lacking. In recent years, approaches and tools have emerged that allow the incorporation of existing knowledge of Design for Additive Manufacturing (DfAM) into the design process. Nevertheless, these applications mostly do not consider the formalisation of both restrictive and opportunistic DfAM guidelines for their integration in design tools.

Therefore, the following article presents a framework for the knowledge-driven adaptation of existing designs in the context of DfAM within an expert system. The novelty of the presented approach lies in the interdisciplinarity between the formalization of design guidelines and their integration and consideration within computeraided design for the semi-automated adaptation of functional non-assembly mechanisms. The application of the presented framework to a case study manufactured via Fused Layer Modeling (FLM) illustrates the applicability and benefits.

Keywords

Additive ManufacturingKnowledge-driven DesignDesign for Additive Manufacturing (DfAM)Ontologies
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2405 - 2414
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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