Prediction of load-dependent power loss based on a machine learning approach in gear pairs with mixed elastohydrodynamic lubrication

Abstract

A machine learning approach was developed to predict load-dependent power loss in gear pairs under mixed elastohydrodynamic lubrication (EHL). Using a homogenized mixed EHL solver, training data were generated, considering surface roughness and cavitation phenomena. A multimodal deep learning (MMDL) model improved regression performance for multimodal inputs. Validation against experimental data confirmed the model's reliability, achieving a maximum gear efficiency error of 0.07 %. The MMDL model with concatenation fusion was selected for its highest R-square value of 0.99166, significantly accelerating simulation speed by 0.05 %. This method can be applied to optimize gear design and provides an efficient solution to reduce power loss in automotive drivetrains, overcoming the limitations of conventional analytical and EHL methods.