Concept for Improving the Traceability of Design Automation through Reinforcement Learning

Utz Y, Götz S, Wartzack S (2025)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2025

Publisher: Fraunhofer IAO

City/Town: Stuttgart

Pages Range: 412 - 423

Conference Proceedings Title: Tagungsband zum Stuttgarter Symposium für Produktentwicklung SSP 2025

Event location: Stuttgart DE

URI: https://elib.uni-stuttgart.de/handle/11682/16385

DOI: 10.18419/opus-16366

Open Access Link: https://elib.uni-stuttgart.de/handle/11682/16385

Abstract

Reinforcement learning (RL) methods can be used for the automation of design adaptations due to their inherent iterative nature. However, they present difficulties with regard to the interpretability of the results. One reason for this is that RL methods are integrated into the product development process as black boxes. As a result, the use of these methods in safety-critical areas such as mechanical design is only possible to a limited extent due to the lack of traceability of the design decisions made by the algorithm. In order to increase the usability of RL-based tools supporting the product developers, the interpretability of the RL-based results must be improved. Therefore, this paper proposes a concept for increasing the traceability of RL-based tools for the automation of design adaptations. Traceability is to be improved by imitating the decision-making process of experts. To this end, the concept is presented using a design adaptation of fibre-plastic composite (FRP) components.

Authors with CRIS profile

Yannick Utz Chair of Engineering Design (KTmfk) Stefan Götz Chair of Engineering Design (KTmfk) Sandro Wartzack Chair of Engineering Design (KTmfk)

How to cite

APA:

Utz, Y., Götz, S., & Wartzack, S. (2025). Concept for Improving the Traceability of Design Automation through Reinforcement Learning. In Katharina Hölzle, Matthias Kreimeyer, Daniel Roth, Thomas Maier, Oliver Riedel (Eds.), Tagungsband zum Stuttgarter Symposium für Produktentwicklung SSP 2025 (pp. 412 - 423). Stuttgart, DE: Stuttgart: Fraunhofer IAO.

MLA:

Utz, Yannick, Stefan Götz, and Sandro Wartzack. "Concept for Improving the Traceability of Design Automation through Reinforcement Learning." Proceedings of the Stuttgarter Symposium für Produktentwicklung 2025, Stuttgart Ed. Katharina Hölzle, Matthias Kreimeyer, Daniel Roth, Thomas Maier, Oliver Riedel, Stuttgart: Fraunhofer IAO, 2025. 412 - 423.

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