Cross-Material Benchmarking of Machine Learning Models for Cutting Force Prediction in CNC Turning

Accurate prediction of cutting force is essential for process optimization and intelligent control in CNC turning, yet cross-material performance comparisons of machine learning models remain limited. This study develops and applies a structured diagnostic benchmarking framework to evaluate ten supervised regression models for cutting force prediction across five engineering alloys: Aluminum Alloy 6061, Brass C26000, Bronze C51000, Stainless Steel 304 (annealed), and Carbon Steel 1020 (annealed). The input space included material category together with machining descriptors (diameter, feed rate, and axial distance from the chuck). Model performance was evaluated using the coefficient of determination (R2), root mean square error (RMSE), cross-validated stability metrics, and pairwise dominance probability matrices derived from R2 and CV(RMSE). Gradient Boosting achieved the highest overall accuracy and robustness, with a mean R2 = 0.962 and RMSE = 18.03 N, followed by a feedforward neural network (R2 = 0.953, RMSE = 19.96 N), while Support Vector Regression showed substantially lower performance (R2 < 0.65; RMSE > 54 N). Residual diagnostics indicated that ensemble and neural models produced compact, near-homoscedastic error distributions, whereas linear and single-tree models exhibited systematic bias and heteroscedasticity. Principal Component Analysis revealed that the first two components captured 78.7% of the total variance, separating geometric and spatial effects from feed-driven variability. The proposed evaluation framework provides a unified methodology for accuracy, and multivariate interpretation in machining force prediction. These results offer practical guidance for selecting robust learning models in intelligent CNC systems and data-driven manufacturing environments.