Modern machining requires reduction in energy usage, surface roughness, and burr width to produce finished or near-finished parts. To ensure high surface quality in machining processes, it is crucial to minimize surface finish and minimize burr width, which are considered as significant parameters as specific cutting energy. The objective of this study was to identify the optimal machining parameters for milling in order to minimize surface roughness, burr width, and specific cutting energy. To achieve this, the research investigated the impact of feed per tooth, cutting speed, depth of cut, and number of inserts on the responses across three intervals using Taguchi L9 array. Observing the responses by varying these parameters, underlined the need for multi objective optimisation. Machining conditions of 0.14 mm/tooth f z , 350 m/min V c and 2 mm ap using 1 cutting insert (exp no 9) was identified as the best machining run using grey relational analysis owing to its highest grey relational grade of 0.936. ANOVA examination identified cutting speed as the leading factor impacting the grey relational grade with 31.07 % contribution ratio, with the number of inserts, depth of cut, and feed per tooth also making notable contributions. Conclusively, machining parameters identified through response surface optimisation resulted in 21.69 % improvement in surface finish, 11.39 % reduction in specific energy consumption, and 6.2 % decrease in burr width on the down milling side albeit with an increase of 9 % in burr width on the up-milling side.

Department of Machining Assembly and Engineering Metrology Mechanical Engineering Faculty VŠB Technical University of Ostrava 17 Listopadu 2172 15 Ostrava 708 00 Czech Republic

Department of Mechanical Engineering College of Electrical and Mechanical Engineering Islamabad 44000 Pakistan

Department of Mechanical Engineering College of Engineering King Faisal University Al Ahsa 31982 Saudi Arabia

School of Mechanical and Manufacturing Engineering Islamabad 44000 Pakistan

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