@inproceedings{KunzEtAl2025_XDPOpt,
  author       = {Leonhard Kunz and Kokulan Thanabalan and Tatjana Legler and Lukas Malburg and Simon Bergweiler and Martin Ruskowski and Ralph Bergmann},
  editor       = {Johannes Wichmann and Pascal Reuss and Lisa Grewenig},
  title        = {{XDP-Opt: Experience-Based Design Process Optimization for Industrial Manufacturing}},
  booktitle    = {Proceedings of the KI Workshop Knowledge Management (FGWM 2025) co-located with the 48th German Conference on Artificial Intelligence (KI 2025), Potsdam, Germany, September 16-19, 2025},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {4209},
  pages        = {42--52},
  publisher    = {CEUR-WS.org.},
  abstract  = {A comprehensive approach to product development involves evaluating all aspects of a product’s lifecycle, from the initial design phase to its end-of-life. Design for Excellence assessment software integrated into a Computer-Aided Design (CAD) environment helps to identify potential issues early, and ultimately avoid costly mistakes during the development of new products. However, this software is predominantly bound to strict rule-based reasoning, which does not account for subtle design constraints and provides no solutions to detected problems. The research project XDP-Opt addresses these problems by developing an Interactive Design Decision Support System. It utilizes a combination of federated foundation models and Case-Based Reasoning to discover problematic design features and suggest solutions for these based on historical CAD data. This paper gives a literature overview on the problem the project is concerned with and shows the core concept and the components of the AI core of the proposed support system.},
  keywords = {Case-Based Reasoning, Design Decision Support, Federated Learning, Foundation Models, Design for Manufacturability},
  year         = {2025},
  url          = {https://www.wi2.uni-trier.de/shared/publications/FGWM_Workshop_2025_XDP-Opt.pdf}
}

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