What is it about?
This article presents a new method for predicting wear in journal bearings by integrating a multiscale contact mechanics model into a mixed elastohydrodynamic lubrication (EHL) framework. The model dynamically updates surface roughness as wear progresses, using a nonlocal averaging function to improve accuracy. This approach allows the simulation of wear depth and surface roughness evolution under different operating conditions, including varying lubricants and loads.
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Why is it important?
Wear is a critical factor affecting the durability of journal bearings in engines and industrial machinery. Predicting wear accurately helps improve component life and optimize lubricant formulations. The proposed method improves wear predictions by accounting for roughness evolution, asperity contact, and lubricant properties, offering a more reliable tool for engineers designing high-performance, durable bearing systems.
Perspectives
This study highlights the importance of including multiscale surface roughness evolution and nonlocal contact models in wear simulations. It shows that such models can more realistically represent real-world wear processes in engine bearings. Future research could focus on extending this approach to transient conditions, different geometries, or real-time predictive maintenance using AI and machine learning.
Dr. Javier Blanco-Rodriguez
Universidade de Vigo
Read the Original
This page is a summary of: A Novel Multiscale Contact Mechanics Approach for Wear Prediction in Journal Bearings via a Mixed Elastohydrodynamic Simulation, Lubricants, May 2025, MDPI AG,
DOI: 10.3390/lubricants13050230.
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