Concept for Improving the Traceability of Design Automation through Reinforcement Learning - Utz, Yannick; Götz, Stefan; Wartzack, Sandro (Tagungsband zum Stuttgarter Symposium für Produktentwicklung SSP 2025, 2025)
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.<br
Keywords: Reinforcement Learning; Design Automation; Traceability; Decision Making