Karadurmus, ErdalAkyazi, HabibGoz, EdaYuceer, Mehmet2024-08-042024-08-0420180193-26911532-2351https://doi.org/10.1080/01932691.2017.1391702https://hdl.handle.net/11616/98028Mineral oil is one of the most important materials on earth and it is used widely for its several features. Mineral oils derived from petroleum products are commonly used to decrease the friction effects in machine parts and, thus, they both prevent wear/overheating and facilitate power transmission. In this study, various binary mixtures of various base oils (SN-80, SN-100, SN-150, SN-50, SN-500) were prepared at different volumetric ratios. Kinematic viscosity (at 40 degrees C and 100 degrees C), viscosity index, flash point, pour point, and density (at 20 degrees C) measurements were performed for characterization of the prepared mixtures. These values were modeled by an artificial neural network (ANN) and the model was tested with root mean squared error (RMSE), mean absolute percentage error (MAPE, %), and regression coefficient (R) values. A higher value of correlation coefficient and smaller values of MAPE and RMSE indicate that the model performs better. For predicting kinematic viscosity at 40 degrees C, correlation coefficients were calculated for training and testing the network as 0.9999 and 0.9995, respectively. Respective MAPE values were determined as 1.011% and 1.8771%. [GRAPHICS] .eninfo:eu-repo/semantics/closedAccessANNcrude oil blendingArticle3991236124310.1080/01932691.2017.13917022-s2.0-85034045408Q3WOS:000441655600002Q4