Artificial intelligence techniques for improving cylindrical shrink-fit shaft-hub couplings
Year: 2023
Editor: Kevin Otto, Boris Eisenbart, Claudia Eckert, Benoit Eynard, Dieter Krause, Josef Oehmen, Nad
Author: Saeed, Muhammad Shahrukh (1,2); Falter, Jan (1); Dausch, Valesko (1); Wagner, Markus (1); Kreimeyer, Matthias (1); Eisenbart, Boris (2)
Series: ICED
Institution: 1: University of Stuttgart;
2: Swinburne University of Technology
Section: Design Methods
Page(s): 0645-0656
DOI number: https://doi.org/10.1017/pds.2023.65
ISBN: -
ISSN: -
Abstract
Due to the continuous progress in information technology, complex problems of machine elements can be investigated using numerical methods. The focus of these investigations and optimizations often aims to reduce the stresses that occur or to increase the forces and torques that can be transmitted. Interference fit connections are an essential machine element for drive technology applications and are characterized by their economical fabrication. The transmission of external loads over a large contact surface between the shaft and hub makes it less vulnerable to impact loads. These advantages contrast with disadvantages such as the limited transmittable power, the risk of friction fatigue, and stress peaks at the hub edges, which can lead to undesirable and sudden failure, especially in the case of brittle hub materials. Analytical approaches already exist for optimizing these connections, which are expensive, time-consuming, and complex, so a high degree of expert knowledge is required to apply these methods in practice successfully. This paper presents a novel method using the example of optimizing the pressure distribution in the interface of a shrink-fit connection.
Keywords: Design engineering, Artificial intelligence, Optimisation, Industrial design