Machine Learning-Based Classification of Suspension Droplet-Solid Wall Impacts for Control of Droplet Fragmentation

This study explores the collision of suspension droplets against solid dry surfaces (substrates). It applies and compares multiple machine learning (ML) models for the classification of impact outcomes: splashing, breaking up, and rebound. A total of 372 experiments were performed with varied particle properties, liquid parameters, substrate properties, droplet velocity, and substrate inclination. The suggested markup indicators form the basis for computer vision processing of drop-wall impact videos. From 12 dimensionless features, seven are selected to train ML classifiers. We tested ten models, including Logistic Regression, KNN, SVM, ensemble methods (CatBoost, XGBoost, LightGBM, Random Forest, AdaBoost), and neural networks (MLP, TabNet). The gradient boosting model CatBoost showed the best results: accuracy and macro-averaged F1-score improved by more than 15% over the baseline, ROC-AUC exceeded 96%. Logistic Regression demonstrated robust generalization capabilities. The key physical finding is that greater sedimentation Stokes number, particle size, and inclination increase the likelihood of splashing and fragmentation. The models allow prediction of no-fragmentation outcomes, important for droplet-based 3D-printing and coating. To the best of our knowledge, this is the first study to use ML for the classification of suspension drop-wall impacts, including data from experiments with both heavier and lighter-than-liquid particles and inclined surfaces. The results obtained assist in controlling suspension droplet impact to avoid fragmentation.