Film Thickness Prediction from Dichromatic Interference Images Based on Dual-Wavelength Physics-Guided Graph Neural Network

Dichromatic optical interferometry provides rich optical information for lubricant film-thickness measurement. However, experimental data are typically limited to a small number of discrete operating conditions, making it difficult to learn the nonlinear relationship among entrainment speed, dichromatic interference images, and lubricant film thickness. To address this limitation, a Dual-Wavelength Physics-Guided Graph Neural Network (DW-PG-GNN) is proposed for intermediate lubrication-state characterization from sparse experimental observations. Rather than directly regressing film thickness from interference images, the proposed framework learns the nonlinear relationship among entrainment speed, dichromatic interference images, and lubricant film thickness through a unified physics-guided learning framework. Specifically, graph representations capture the structural continuity and cross-wavelength coupling characteristics of dichromatic interference fringes, while a physics-guided residual learning strategy embeds analytical film-thickness priors from classical elastohydrodynamic lubrication (EHL) theory into an implicit neural representation for intermediate state prediction. A differentiable interference renderer further constrains the consistency between predicted film thickness and reconstructed dichromatic interference images, ensuring optical and physical consistency throughout the learning process. Validation based on sparse experimental datasets acquired from a ball-on-disc EHL rig demonstrates reliable average film-thickness prediction and interference-image reconstruction under the investigated operating conditions. The prediction errors remain within 2.2% for trained conditions and 8.67% for unseen entrainment-speed interpolation conditions.