Abstract This study presents a systematic comparison of six machine learning algorithms for surface roughness (Ra) prediction in dry turning of Ti-6Al-4V across two nose radii (r = 0. 4 mm and r = 0. 8 mm) using a central composite design with 38 experiments. The algorithms evaluated are Support Vector Regression, Random Forest, XGBoost, Gaussian Process Regression, Extreme Learning Machine, and a Lichtenberg-Optimised ELM in which the Lichtenberg Algorithm replaces random weight initialisation with a structured fractal search. All models are trained on (Ra) and validated by leave-one-out cross-validation under two feature configurations: process parameters only and process parameters augmented with cutting force measurements. GPR achieves the highest accuracy (R^2 = 0. 922) ; under the present conditions, process parameters alone prove sufficient — force signals degrade four of six models due to collinearity with feed rate, confirmed by variance inflation factors exceeding 10 for F₅ and F. A polynomial regression baseline reaches R^2 = 0. 920, matching GPR; ML advantages lie in uncertainty quantification, localised nonlinearity capture, and robustness to broader conditions. LA-ELM records the largest force-induced gain (R^2 = +0. 074), reaching R^2 = 0. 895 and surpassing Random Forest and XGBoost. Results confirm that the Lichtenberg Algorithm is an effective weight optimisation strategy for ELM in small-dataset manufacturing regression.
Leandro et al. (Mon,) studied this question.