A Study on Prediction of Friction Characteristics from Speckle Patterns of Friction Surfaces Using Machine Learning

Abstract

The accurate prediction of friction coefficients is crucial for the maintenance of sliding mechanical components to enable the timely detection of potential failures. Traditional methods rely on sensors like load cells and strain gauges to measure friction coefficients. However, these conventional techniques face challenges in real-time measurement during machine operation owing to physical constraints associated with sensor placement. To address this limitation, this study investigates the application of laser speckle patterns for predicting friction coefficients through a novel approach using convolutional neural networks (CNNs). The laser speckle technique offers rich surface condition data, while CNNs, which are particularly advanced in managing vast datasets, excel in establishing relationships between diverse factors for precise inference, classification, and prediction. Utilizing ResNet, a leading CNN architecture, a new friction tester capable of concurrently recording friction coefficients and speckle patterns in a cylinder-on-disk friction test was developed. The findings reveal that the CNN-based method, especially with ResNet, attained a coefficient of determination (R²) of 0.758, demonstrating its effectiveness in the accurate prediction of friction coefficients. This study significantly advances the field of friction coefficient prediction, highlighting the innovative application of laser speckle methodologies combined with machine learning techniques.