The impact of machining factors such as spindle speed, depth of cut, and feed rate on the surface quality achieved in CNC end milling is explored. In this study, the Taguchi, and response surface method (RSM), techniques were combined to predict optimal machining settings for AL6061 Aluminium alloy with the lowest surface roughness and high material removal rate (MRR) values. The effectiveness of computer numerical control processing parameters which include spindle speed, feed rate, and depth of cut on arithmetic average roughness (Ra) and MRR was investigated using a design of experiment. A stylus was used to measure the average surface roughness values of the samples. Using Taguchi L27 orthogonal array Design of experiment was generated then a second-order response surface regression mathematical model was generated and finally optimized using box-bohen design response optimizer using desirability function. In three steps, the best machining conditions for reducing Ra and MRR were determined. The proposed strategy provides an efficient way for minimising surface roughness and maximising material removal rate, according to experimental data for AL6061 Aluminium alloy.
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