A key challenge in advanced machining is the optimization of process parameters to satisfy both the manufacturer's and consumers’ needs. In aerospace and automotive applications, assessing workpiece surface condition in drilling is complex, as drill hole quality is affected by chip length. Ensuring reliability requires effective machining and applying optimization techniques. This work presents the methodical analysis and optimization of material removal rate (MRR) and surface quality during the drilling of aluminum 7075 (Al7075). Al7075 alloys, renowned for their exceptional electrical performance and mechanical properties, are frequently utilized as workpieces in a cost-effective CNC drilling process. In this process, surface roughness (SR) and MRR are influenced by cutting depth, feed rate, and speed. In general, chip control often receives limited attention, leading to the formation of large chips that are easily and reliably removed from the workpiece. The primary machining factors influencing chip control are attaining a desirable surface finish and maintaining a high MRR. An orthogonal array Taguchi L9 is used to compute the experiment count. The cutting parameters for minimum SR and maximum MRR were optimized using grey fuzzy relational analysis. The fuzzy model developed in this study exhibits excellent reliability and accuracy, making a valuable contribution to the progress of machining technology. The results of the experiment testing indicated that the average of the five longest chip lengths was 114 mm, and the MRR value was 19.20 gm/min.
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