Surface roughness prediction in dry turning of Ti-6Al-4V: benchmarking six machine learning algorithms under cutting force integration with a Lichtenberg-optimised ELM

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_ε = 0.4 mm and r_ε = 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 log (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 F̄_f and F̄_p . 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 ( Δ 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.