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.
Formalizing Machining Feature Knowledge For Feature-Based Design Automation In CAD
Berger, Elias; Pusch, Felix; Herrmann, Kevin; Becker, Christian; Mehlstäubl, Jan; Lachmayer, Roland; Paetzold-Byhain, Kristin
Type:
Year:
2026
Editor:
Juuti, T.; Karhunen, V; Vanhatalo, M.; Lehtonen, T.;Kopra, M-J.
Author:
Berger, Elias; Pusch, Felix; Herrmann, Kevin; Becker, Christian; Mehlstäubl, Jan; Lachmayer, Roland; Paetzold-Byhain, Kristin
Series:
NordDESIGN
Institution:
Dresden University of Technology, Germany; MAN Truck & Bus SE, Germany; Leibniz Universität Hannover, Germany
Page(s):
341-350
DOI number:
ISBN:
978-1-912254-21-7
ISSN:
NA
Abstract:
Parametric CAD models encode engineering design knowledge through feature-based representations, yet generative AI approaches to design automation treat these models as geometric shapes, discarding functional semantics and manufacturing constraints. Text-to-CAD methods rely on vision-language models to generate descriptions from renders, producing annotations that capture visual appearance but lack engineering content, limiting their ability to generate functionally valid outputs. We present a methodology for capturing machining feature knowledge in parametric CAD representations. Our approach enriches models with structured metadata linking geometric features to functional purposes and manufacturing requirements while maintaining traceability to design operations. We validate this on 1,770 shaft components with dual annotations—VLM-only baseline versus feature-augmented. Training generative models on feature-augmented data yields substantial improvements in geometric accuracy and functional correctness, demonstrating that structured engineering metadata enables better capture of design intent. We release our code for reproducibility.
Keywords:
