CUTTING PARAMETER OPTIMISATION FOR SURFACE QUALITY AND ENERGY EFFICIENCY IN CNC MACHINING: A SYSTEMATIC LITERATURE REVIEW

The choice of cutting speed, feed rate and depth of cut not only affects the surface finish of the end product of the CNC machine, but also the machine tool's electricity consumption and machining time. Trimming parameter selection is a multi-objective problem because any parameter set that improves one of these responses tends to be detrimental to the other. This paper is a systematic literature review (SLR) based on PRISMA 2020 guidelines that covers and summarizes the research related to cutting-parameter optimisation for surface quality and energy efficiency in CNC turning, milling, drilling, honing and wood machining. The literature was searched in English in Google Scholar, Web of Science and ScienceDirect from January 2016 to June 2026, with backward citation searching for seminal earlier work. The included studies were categorised according to the machining process, cutting parameters, surface and energy responses and the optimisation method, and then synthesised in narrative form. The evidence indicates that feed rate is the most dominant parameter to control surface roughness in most of the studies conducted on turning and milling, depth of cut is the major parameter that affects the power consumption and material removal rate, and cutting speed affects both the surface roughness and the power consumption and tool wear, with better surface finish in a range of cutting speeds specific to the material being turned and better power consumption and tool wear at higher cutting speeds. The optimum (minimal) energy and optimum (minimal) material removal are often different, and minimum power settings are not always minimum energy settings. From single response Taguchi–ANOVA designs, optimisation practice has shifted towards grey relational analysis, TOPSIS, response surface models, artificial neural networks and Pareto based metaheuristics like NSGA-II. There are key challenges in the treatment of tool wear and embodied energy, in-process adaptive optimisation, validation on industrial machines and resource-constrained manufacturing economies. Future work is proposed using a conceptual framework, which connects cutting parameters, responses, optimisation methods and Industry 4.0 feedback.