Artificial intelligence-based predictive modeling of surface roughness in external turning of C45 steel

In this study, the effects of cutting parameters, namely cutting speed (v), feed rate (f), depth of cut (t), and machining diameter (d), on surface roughness in external turning of C45 steel were investigated using the Taguchi method. Taguchi analysis, Random Forest, and ANOVA were employed to identify the factors affecting surface roughness. The results revealed that feed rate had the most significant effect, followed by machining diameter, depth of cut, and cutting speed. Four regression models, including polynomial regression, Random Forest Regression (RFR), Artificial Neural Network (ANN), and Extreme Learning Machine (ELM), were developed to predict surface roughness based on cutting parameters. Among them, the ELM model demonstrated the highest prediction accuracy, characterized by a high coefficient of determination (R2) value and low mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE). Therefore, the ELM model is considered the most suitable for predicting surface roughness in precision external turning operations.