Stress Analysis of SKF 6206 Bearing under Radial Load using Theoretical and Numerical Evaluation

Department of Mechanical Engineering, KIT’s College of Engineering (Empowered Autonomous) Kolhapur, 416234 Maharashtra, India

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Published online: 3 March 2026

Abstract

Deep groove ball bearings play a critical role in rotating machineries. Their structural performance under radial and axial loading is vital for reliability. Static analysis is important to understand deformation, stress distribution, and load capacity to avoid premature failures. This study discusses about performing Static analysis and validating Finite element analysis (FEA) with established theoretical results under radial load. A 3D model of ball bearing is created in ANSYS SpaceClaim and static structural analysis is performed using ANSYS software to simulate deformations and stresses. The extracted results particularly maximum contact stresses on the inner raceway and the static load distribution between the rolling elements were compared with the classical Hertzian contact theory and analytical load distribution formulas. With minimal deviation in stress values, FEA results showed similarity with theoretical calculations. The study gives the confirmation about FEA as highly reliable tool for predicting static behavior of deep groove ball bearings, giving a solid foundation for design validation and dynamic analysis. Conventional maintenance practices, such as scheduled inspections or time-based replacements, are still common in industries. While these approaches can prevent sudden breakdowns to some extent, they are not always efficient in identifying incipient faults at an early stage. Once a fault is allowed to progress, it becomes much harder to manage and may demand complete machine stoppage for repair. This limitation has motivated the development of more reliable, data-driven approaches, where vibration analysis combined with machine learning is seen as a promising solution.