Knowledge-driven Design for Additive Manufacturing: A framework for design adaptation

DS 122: Proceedings of the Design Society: 24th International Conference on Engineering Design (ICED23)

Year: 2023
Editor: Kevin Otto, Boris Eisenbart, Claudia Eckert, Benoit Eynard, Dieter Krause, Josef Oehmen, Nadège Troussier
Author: Schaechtl, Paul (1); Goetz, Stefan (1); Schleich, Benjamin (2); Wartzack, Sandro (1)
Series: ICED
Institution: 1: Friedrich-Alexander-Universität Erlangen-Nürnberg; 2: Technische Universität Darmstadt
Section: Design Methods
Page(s): 2405-2414
DOI number:


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 Manufacturing, Knowledge-driven Design, Design for Additive Manufacturing (DfAM), Ontologies

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