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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.

AI-enhanced computer-aided design: predictive modelling of operations

Steininger, Sarah (1,2); Kezer, Saltuk (1,2); Krüger, Moritz (1,2); Richtsfeld, Robin (3); Fottner, Johannes (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:
Steininger, Sarah (1,2); Kezer, Saltuk (1,2); Krüger, Moritz (1,2); Richtsfeld, Robin (3); Fottner, Johannes (1)
Series:
DESIGN
Institution:
1: Technical University of Munich, Germany; 2: BMW Group, Germany; 3: University of Passau, Germany
Page(s):
2561-2570
DOI number:
10.1017/pds.2026.10614
ISSN:
2732-527X (Online)
Abstract:
This work introduces a graph-based CAD assistant that predicts the next modelling operation in parametric design sequences. Real CATIA V5 models from the automotive domain are converted into directed acyclic graphs capturing feature dependencies, enabling learning directly from structural design data. A four-layer Graph Attention Network achieved a top-5 prediction accuracy of 94%, outperforming a frequency-based non-parametric baseline. The results show that graph representations and attention-based message passing provide a strong foundation for context-aware modelling assistance.
Keywords:
computer-aided design (CAD), artificial intelligence (AI), graph neural networks, AI-assisted design, parametric feature graphs
DESIGN2026 - The 19th International Design Conference
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