Experimental Investigation and Predictive Modeling of Surface Roughness in Dry Turning of AISI 1045 Steel Using Power-Law and Response Surface Approaches

Dry machining of AISI 1045 steel is attractive for sustainable manufacturing but makes it more challenging to control surface roughness Ra. This work investigates dry turning of AISI 1045 using a 23 factorial design with three center points (11 runs) and compares a traditional power-law correlation with a quadratic response surface model (RSM). The power-law fit on log-log data explains only about 20% of the variance, whereas the quadratic RSM achieves R2 ≈ 0.98 with a root-mean-square error (RMSE) of 0.62–0.77 µm based on leave-one-out cross-validation and bootstrap resampling. Feed rate S is identified as the dominant factor, while cutting speed V and depth of cut t have secondary but non-negligible interactive effects. Sobol global sensitivity indices confirm that S and S2 account for more than half of the output variance. The optimized setting within the tested domain (V ≈ 83 m/min, S = 0.60 mm/rev, t = 0.10 mm) yields a predicted Ra ≈ 5.3 µm, appropriate for semi-roughing prior to grinding. The proposed framework combines small-sample RSM, Lasso regularization, uncertainty quantification and Sobol analysis to provide an uncertainty-aware model for optimizing dry-turning parameters of AISI 1045 steel.