An AI-Based Approach to Optimize Stress in Shrink Fits

Dausch, V.; Kröger, J.; Kreimeyer, Matthias

doi:10.1017/pds.2022.157

Cited by 2 publications

(10 citation statements)

References 9 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Q-values are stored in a table whose tabular structure allows these values to be assigned to a unique combination of n states z1 to zn (rows) and m capable actions a1 to am (columns). The agent thus independently learns the difference between good and wrong actions over the iterations so that the algorithm's performance increases with the number of iterations executed (Dausch et al, 2022).…”

Section: Artificial Intelligence Methods To Optimize Shaft-hub Connec...mentioning

confidence: 99%

“…Reinforcement learning is an agent-based process in which the agent awards a reward for actions previously taken. This method aims to maximize the reward function's value and find the optimal result (Skansi, 2018;Dausch et al, 2022;Montague, 1999).…”

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

“…These excessive stiffness jumps initially cause stress peaks with different consequences, such as material failure (brittle materials), unintentional plasticizing (low-strength materials), or reduced joint fatigue strength. Previous studies, such as (Wagner and Binz, 2011), (Falter et al, 2022) and (Dausch et al, 2022), have produced several strategies based on geometry and material for optimizing shrink-fits. Due to their incredible complexity and lack of generic computation methodologies, these approaches are unsuitable for widespread use (Pedersen, 2016).…”

Section: Research Problem and Objectivesmentioning

confidence: 99%

“…A tangential tension is produced by the hub's elastic expansion as well. The joint is primarily pressured by shear forces caused by friction in the axial direction (Dausch et al, 2022;Leidich, 2008).…”

Section: Optimization Of Stress For Shrink-fit Connectionsmentioning

confidence: 99%

“…Due to brittle materials or excessive radial and tangential stresses caused by stiffness jumps or external loads, the connection may then fail. The artificial intelligence (AI)-powered automated shaft-hub connection design can produce a user-friendly, less complex, and more time-efficient optimization, as shown in (Dausch et al, 2022). Nevertheless, there still needs to be more performance due to the dependency on iterative and, therefore, still time-consuming finite element (FE) simulations.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Artificial Intelligence Techniques for Improving Cylindrical Shrink-Fit Shaft-Hub Couplings

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Section: Artificial Intelligence Methods To Optimize Shaft-hub Connec...mentioning

confidence: 99%

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Section: Research Problem and Objectivesmentioning

confidence: 99%

Section: Optimization Of Stress For Shrink-fit Connectionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Artificial Intelligence Techniques for Improving Cylindrical Shrink-Fit Shaft-Hub Couplings

et al. 2023

View full text Add to dashboard Cite

show abstract

An Investigation of Increased Power Transmission Capabilities of Elastic–Plastic-Designed Press–Fit Connections Using a Detachable Joining Device

Falter,

Herburger,

Binz

et al. 2024

Eng

View full text Add to dashboard Cite

Drive systems are an important part of general mechanical engineering, automotive engineering, and various other fields, with shaft–hub connections being an important part of such systems. Decisive aspects in the development of such systems today are, for example, high transmittable forces and torques, low masses, and the cheapest possible production of components. A possibly threefold increase in the force and torque transmission capacity can be achieved by using press–fit connections with an elastic–plastic design as opposed to regular elastically designed alternatives. An elastic–plastic design of the press–fit connection is achieved by using a large interference. A large transition geometry on the shaft (which replaces the conventional chamfer) is required to join such an interference. The material and space requirements have a negative impact on lightweight applications and limited building spaces. Therefore, the objective of the research presented in this paper is to design and analyze a detachable joining device that substitutes this geometry. A simulation study was conducted to determine the geometry of the joining device that improves the stress state and consequently the force and torque transmission capacity of the connection. Moreover, the influence of manufacturing tolerances of the joining device and the shaft, corresponding risks, and measures to mitigate them are analyzed using finite element analysis. The results show that large transition radii, enabled by using a joining device, lead to a homogenous distribution of plastic strain and pressure in the press–fit connection, even for large interferences ξ and soft hub materials like wrought aluminum alloys. The influence of manufacturing tolerances on the stress state was quantified, leading to design guidelines that minimize the risk of, e.g., the front face collision of a shaft and hub, while maximizing the power transmission of the connection. The results show the capability of a detachable joining device to enable elastic–plastic press–fit connections and the corresponding threefold increase in the force and torque transmission capacity in lightweight applications, resulting from the substitution of the installation space consuming and mass increasing the transition geometry of the shaft.

show abstract

An AI-Based Approach to Optimize Stress in Shrink Fits

Cited by 2 publications

References 9 publications

Artificial Intelligence Techniques for Improving Cylindrical Shrink-Fit Shaft-Hub Couplings

Artificial Intelligence Techniques for Improving Cylindrical Shrink-Fit Shaft-Hub Couplings

An Investigation of Increased Power Transmission Capabilities of Elastic–Plastic-Designed Press–Fit Connections Using a Detachable Joining Device

Contact Info

Product

Resources

About