Machine-learning surrogates for the multi-objective optimisation of cutting parameters in dry turning of POM C GF25%

This work investigates the dry turning of polyoxymethylene reinforced with fibreglass (POM C GF25%) with a carbide insert, following an orthogonal Taguchi experimental plan. The influence of the four cutting parameters (nose radius r, cutting speed, depth of cut and feed f) on surface roughness (), tangential cutting force (), cutting power () and cutting energy () is first quantified by ANOVA and described by linear-with-interactions RSM polynomials, which serve as an interpretable parameter-effect analysis. Five machine-learning regressors (SVR, Random Forest, Gradient Boosting, XGBoost and a Deep Neural Network) are then trained on the same data and compared by five-fold cross-validation; Gradient Boosting and XGBoost emerge as the best generalisers and are retained as per-output surrogates (, : GB;, : XGBoost). A Gaussian-process smoothing of these surrogates is used as the objective function of four metaheuristic algorithms (MOALO, MOGWO, MODA and MOGOA) to construct three bi-objective Pareto fronts: (,), (,) and (,). The four algorithms produce essentially overlapping fronts. The two main contributions of the study are (i) the experimental characterisation and modelling of POM C GF25% machining and (ii) the use of cross-validated ML surrogates, in place of the conventional RSM polynomials, as the objective function of the multi-algorithm metaheuristic search.