A Hybrid Regression and Machine Learning-Based Multi-Output Predictive Modeling of Cutting Forces and Surface Roughness in Rotational Turning of C45 Steel

Rotational turning is a hybrid machining process that combines features of milling and conventional turning, resulting in altered chip formation and force generation mechanisms. Despite its technological relevance, the predictive modeling of cutting forces and surface roughness in rotational turning has received little attention. This study applies and evaluates a hybrid regression and machine learning modeling for the multi-output prediction of three cutting force components and two surface roughness parameters during rotational turning of normalized C45 steel. The input variables are tool inclination angle, depth of cut, feed, and cutting speed. Three modeling approaches are compared: stepwise polynomial regression, Gaussian Process Regression, and Random Forest regression, using repeated five-fold cross-validation with ten repetitions. The results show that Gaussian Process Regression provides the highest predictive accuracy for most outputs, particularly for axial and radial forces and roughness parameters, while stepwise regression achieves comparable performance for tangential force with greater interpretability. Random Forest regression exhibits lower accuracy under the structured experimental design. The study demonstrates that combining interpretable regression with probabilistic machine learning enables the accurate prediction of process responses in rotational turning. The proposed methodology represents a novel, statistically validated approach for multi-output modeling of this machining process and supports future applications in process optimization and adaptive manufacturing systems.