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An AI-Based Approach to Optimize Stress in Shrink Fits

Published online by Cambridge University Press:  26 May 2022

V. Dausch ,
J. Kröger  and
M. Kreimeyer
V. Dausch
Affiliation:
University of Stuttgart, Germany
J. Kröger*
Affiliation:
University of Stuttgart, Germany
M. Kreimeyer
Affiliation:
University of Stuttgart, Germany
*
jan.kroeger@iktd.uni-stuttgart.de

Abstract

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The present analytical design of shrink fits typically results in an uneven stress condition that can lead to failure in a variety of manners. With increasing loads and the use of brittle materials, the optimization of the stresses in the shrink fit hence becomes increasingly necessary. Currently existing approaches do not solve the problem satisfactorily or increase the manufacturing and design effort. This paper therefore considers the implementation of an AI-based stress optimization using reinforcement learning, which performs stress optimization by geometrically contouring the interstice.

Keywords

artificial intelligence (AI)engineering designnumerical methodsoptimisationstructural analysis
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1549 - 1558
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

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