Enhancing lubrication performance with novel artificial intelligence-driven film thickness forecasting

Elastohydrodynamic lubrication (EHL) is crucial for the longevity and performance of machine parts subjected to high loads and speeds. Analyzing EHL thickness settings with accuracy is crucial for improving construction and mitigating premature wear. Current approaches for forecasting such variables might be limited in their precision due to computation difficulties. This study presents a novel remora-optimized Gaussian process regression (RO-GPR) approach in calculating the factors related to EHL film dimension, addressing the requirement for a more effective and precise predictive model. The suggested RO-GPR approach is trained and verified by utilizing a dataset that was acquired through methods that are either simulation-based or practical. To show how well the suggested RO-GPR strategy handles the complex structure of EHL mechanisms, its efficacy is compared with that of other approaches. The study's findings reveal that the suggested RO-GPR model can predict EHL film thickness properties with an excellent level of accuracy, indicating its potential as a useful tool for tribological research. By providing insights that can improve the efficiency and construction of machine parts exposed to EHL circumstances, this research advances predictive modeling methodologies for lubrication mechanisms.