Laser surface texturing has emerged as an effective approach to enhance tool–workpiece interactions and improve machining performance. In the present work, a combined experimental and optimisation framework is aimed at assessing the energy-efficient machining of AISI H13 steel using Nd: YAG laser surface-textured cemented carbide tools under various conditions. Turning experiments were conducted on a semi-automatic lathe, and the machining responses were recorded using a dynamometer setup. The regression models were formulated from the conducted experiments and used as surrogate objective functions for optimisation. The Archimedes Optimisation Algorithm (AOA) was considered for single-objective optimisation to find optimal machining conditions for minimum cutting force, minimum coefficient of friction, and maximum material removal rate. To explore the trade-offs associated with conflicting objectives, Pareto optimisation was employed using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), yielding a Pareto front and a balanced (knee-point) solution. The AOA-based balanced solution was obtained at cutting speed (CS) = 75 m/min, feed rate (FR) = 0.20 mm/rev, and depth of cut (DC) = 0.42 mm, yielding cutting force (Fz) = 149.9 N, coefficient of friction (CoF) = 0.320, and material removal rate (MRR) = 0.404 kg/s. In comparison, the NSGA-II knee-point solution at CS = 72 m/min, FR = 0.15 mm/rev, and DC = 0.41 mm resulted in Fz = 123.66 N, CoF = 0.3515, and MRR = 0.3149 kg/s, providing more balanced and stable machining conditions. The quantitative comparison revealed that the NSGA-II provided better optimal solutions, resulting in a 17.5% reduction in cutting force, 2.5% reduction in Specific Consumption Energy (SCE) compared to the AOA-balanced solution, with a balanced increase in friction and a meticulous reduction in material removal rate, thereby representing improved process stability and energy efficiency. The findings are directly related to industrial machining of AISI H13 steel used in die and mould manufacturing, where improved energy efficiency and process stability are critical.
N et al. (Wed,) studied this question.