Python Based Optimization of Cutting Parameters in CNC Turning of AL-6XN Stainless Steel
Abstract
This study optimizes the cutting parameters (V, F, t) to reduce surface roughness Ra during CNC turning of the difficult-to-machine AL-6XN steel, using a second-order experimental design methodology (15 experiments) and Python tools for analysis. A quadratic regression model with a coefficient of determination R2=0,9843 and a Mean Squared Error MSE=0,001695 was successfully developed to describe the relationship between the cutting parameters cutting speed V (m/min), feed rate F (mm/rev), and depth of cut t(mm) and the surface roughness Ra, while also showing that the order of influence is F > t > V. The optimal set of parameters (V=105,2 m/min, F=0,081 mm/rev, t=0,96 mm) yielding a predicted Ramin ≈ 0,713 μm has been determined and experimentally verified. The findings provide an effective methodology and practical optimal parameters for improving the surface quality of AL-6XN during CNC turning.
