Application of machine learning for predicting the incubation period of water droplet erosion in metals.

Water droplet erosion (WDE) is a critical degradation phenomenon that significantly affects component lifespan and performance in power generation, aerospace, and wind energy industries. The incubation period-the initial phase before visible material loss occurs-is particularly crucial for maintenance planning and material selection yet remains challenging to predict accurately due to the complex interplay of material properties and impact conditions. Traditional empirical models have shown limited predictive capability due to their reliance on numerous adjustable parameters with insufficient physical interpretation. This study aimed to develop and validate a machine learning (ML) approach for accurately predicting the WDE incubation period across different metallic materials and impact conditions. The performance of various ML algorithms is evaluated while investigating the effect of data transformation techniques on prediction accuracy. A range of ML models-linear regression (LR), decision tree regressor (DT), random forest regressor (RF), gradient boosting regressor (GBR), and artificial neural networks (ANN)-were trained and validated using experimental data from five different alloys under various impact conditions. Data transformation methods significantly enhanced model performance, with the LR model using Box-Cox transformation achieving the highest accuracy (R