20.500.12556/RUL-188115 Machine learning analysis of droplet spreading and splashing for various liquids and different surface wettability Accurate prediction of droplet impact behavior is essential for applications including spray cooling, coating technologies, additive manufacturing, and inkjet printing. Conventional analytical and empirical models often have limited predictive capability because of the nonlinear interactions among liquid properties, impact conditions, and surface wettability. This study develops machine learning models to predict the maximum spreading coefficient and the critical spreading–splashing threshold velocity using an experimental dataset of more than 700 droplet impacts spanning multiple liquids and hydrophilic, hydrophobic, and superhydrophobic surfaces. Gaussian process regression (GPR) achieved the highest predictive accuracy, predicting the maximum spreading coefficient with coefficients of determination exceeding 0.99 and outperforming widely used empirical correlations. Evaluation using an externally sourced dataset demonstrated satisfactory model transferability, while a second GPR model accurately predicted the critical spreading–splashing threshold velocity within the investigated parameter space. Shapley Additive Explanations (SHAP) and Individual Conditional Expectation (ICE) analyses showed that impact velocity is the dominant predictor of maximum spreading, whereas surface tension primarily governs splash onset, consistent with established droplet-impact physics. These results demonstrate that interpretable machine learning models provide accurate, physically meaningful predictions across diverse liquid–surface systems, in regimes where the input parameters are not scarcely populated, and offer an alternative to conventional empirical correlations. droplet impact machine learning maximum spreading coefficient splashing threshold trk kapljice strojno učenje maksimalni koeficient širjenja kapljice meja razpada kapljice true false true Angleški jezik Slovenski jezik Članek v reviji 2026-09-18 11:43:48 2026-09-18 11:43:57 2026-09-19 04:30:09 0000-00-00 00:00:00 2026 0 0 25 str. iss. 9, art. 262 Vol. 8 2026 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 532:004.85 2413-4155 10.3390/sci8090262 291538691 https://www.mdpi.com/2413-4155/8/9/262 1 6e043e37-b345-11f1-8bc5-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=248128 Panjan_..._Machine.pdf Panjan_..._Machine.pdf 1 0FDCCCFF27B10ADF448EDE03A638F4F7 f867d2a9f9a4c92bee259a43e0b5dbe877b2594218dc0a0c7991d1d4bf587231 d9bfd358-b346-11f1-8bc5-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=248135 Fakulteta za strojništvo 0 0 0