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Knowledge Base Repository

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.

From geometry to function: towards context-aware generative AI for engineering design

Berger, Elias (1,2); Herrmann, Kevin (3); Pusch, Felix (3); Kriesell, Tobias (3); Gembarski, Paul (3); Mehlstäubl, Jan (2); Lachmayer, Roland (3); Paetzold-Byhain, Kristin (1)

Type:
Papers
DS 145: Proceedings of the DESIGN2026 19th International Design Conference
Year:
2026
Editor:
Štorga, M.; Škec, S.; Martinec, T.; Marjanović, D.; Pavković, N.
Author:
Berger, Elias (1,2); Herrmann, Kevin (3); Pusch, Felix (3); Kriesell, Tobias (3); Gembarski, Paul (3); Mehlstäubl, Jan (2); Lachmayer, Roland (3); Paetzold-Byhain, Kristin (1)
Series:
DESIGN
Institution:
1: Dresden University of Technology, Germany; 2: MAN Truck & Bus SE, Germany; 3: Leibniz University Hannover, Germany
Page(s):
2193-2202
DOI number:
10.1017/pds.2026.10577
ISSN:
2732-527X (Online)
Abstract:
Current generative artificial intelligence for Computer-Aided Design (CAD) optimizes for geometric similarity, neglecting engineering criteria like functionality, manufacturability, and sustainability. This paper addresses this gap and proposes a conceptual framework to reorient generative CAD from replicating shapes to achieving function. We introduce two hybrid training strategies: a pre-learning approach using synthetically labeled datasets (evaluated via FEA, CAM, LCA) and a self-learning approach where GenAI uses these knowledge-based tools as a reinforcement feedback loop.
Keywords:
artificial intelligence (AI), computer-aided design (CAD), design automation
DESIGN2026 - The 19th International Design Conference
Conferences

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