Advancements in technology and numerical methods have shifted from slow, resource-intensive software to faster predictive solutions powered by artificial intelligence (AI). An exemplary case is the analysis of interference fit connections between a cylindrical shaft and hub, which has the potential to redefine optimal design, minimizing stress and maximizing torque transmission. Traditional experimental analysis using Finite Element Method (FEM) simulations is undeniably time-consuming, inefficient, and complex, thus necessitating the deployment of AI as a pivotal tool in industrial applications. This paper unequivocally introduces a cutting-edge technique that harnesses two powerful AI approaches: Supervised Learning and Reinforcement Learning. The Reinforcement Learning approach expounded in this paper impeccably predicts the shaft-hub geometry set, eliminating the need for iterative simulations and drastically streamlining the optimization process. In order to address this challenge, a Supervised Learning model is rigorously trained using limited data obtained from experimental structural analysis. Subsequently, the predictions from this model serve as the environment for the Reinforcement Learning (RL) algorithm. The customized environment in Reinforcement Learning ingeniously employs the model to refine predictions by adjusting the input parameters for different geometric sets through respective actions on the environment.
Reinforcement Learning to Improve Finite Element Simulations for Shaft and Hub Connections
ARENA2036
Stuttgart Conference on Automotive Production ; 2024 ; Stuttgart, Germany November 21, 2024 - November 22, 2024
Advances in Automotive Production Technology – Digital Product Development and Manufacturing ; Chapter : 26 ; 335-343
2025-06-20
9 pages
Article/Chapter (Book)
Electronic Resource
English
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