• PRATHYUSH M.V – STM21ME011

• VISHAL MAHESH – STM21ME015

• SREERAJ M – LSTM21ME018

The study presents a comprehensive framework for optimizing CNC turning of Aluminium 2024, a copper–magnesium alloy widely used in the aerospace industry due to its high ductility and strength. The objective of the work is to minimize production time, maximize surface quality, and reduce tool wear by investigating the influence of cutting speed, feed rate, depth of cut, and tool geometry. A genetic algorithm is employed to optimize these parameters, while predictive modeling is used to estimate surface roughness, material removal rate (MRR), and cutting forces based on empirical formulas and cutting force coefficients from literature. Simulations carried out in MATLAB under varying operational conditions identify optimal parameter combinations that improve machining efficiency, surface finish, and productivity. The results demonstrate that simulation-based optimization can significantly refine CNC turning processes, reduce tool wear, minimize cycle times, lower material wastage, and conserve energy, thereby contributing to sustainable manufacturing practices.