Optimization of Cutting Parameters for Sustainable Machining of Titanium Ti-5553_x000D_ Alloy using Genetic Algorithm

Abstract

Titanium Ti-5553 alloys have been considered as difficult-to-machine materials due to the extremely high tool wear, high_x000D_ cutting forces, high temperature, and poor surface quality of machined parts. Process parameters needs to be optimized in order_x000D_ to improve machining performance and in the meantime reducing manufacturing cost. This study proposes sustainable_x000D_ machining process for this new generation Titanium Ti-5553 alloy. Process parameters including depth of cut, cutting speed,_x000D_ and feed rate were taken into account to optimize parameters better tool life, material removal rate and surface roughness_x000D_ together with energy consumption for the first time in literature. Genetic algorithm was utilized for optimizing the process_x000D_ parameters. Obtained results illustrated that optimization using genetic algorithm is a very effective approach to substantially_x000D_ improve machining performance of this alloy and make machining process of this alloy more sustainable by reducing energy_x000D_ consumption, manufacturing cost and increasing material removal rate in machining process of new generation titanium alloy._x000D_