Optimization of Cutting Parameters for Surface Roughness in Aluminium CNC Machining

Industrial products especially in automobile and aerospace sectors require highly precise surface finishes because these factors determine performance limits and end-product appearance. The research deals with cutting parameter optimization to reduce roughness on Aluminium Al6061 parts during CNC machining. Aluminium stands out due to its superior strength-to-weight properties and rust resistance and easy machining nature as metal producers select it for precise finish requirements in their industrial operations. The research investigates the impact of spindle speed together with feed rate and radial depth of cut and axial depth of cut through systematic modification of their machining parameters. The research utilizes a set of 30 experimental trials for surface roughness measurements through contact-type roughness testing of each tested configuration. The prediction and optimization process achieves enhancement with the implementation of Fuzzy Inference System (FIS). Fuzzy logic in FIS models creates nonlinear mapping between process inputs and output quality through its integration of fuzzy membership functions and rule-based inference systems and centroid-based output defuzzification to generate precise predictions. The statistical assessment of each input factor relies on Response Surface Methodology (RSM) and Least Squares Approximation (LSA) and Analysis of Variance (ANOVA) methodologies. The MATLAB-executed implementation of the model achieves dependable prediction accuracy during experimental data comparison. The study demonstrates that soft computing optimization methods lead to successful parameter optimization which produces better surface quality and decreased manufacturing duration and improved component output during aluminium CNC processing operations.