Optimization of Cutting Parameters for Sustainable Machining of Titanium Ti-5553 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 cutting forces, high temperature, and poor surface quality of machined parts. Process parameters needs to be optimized in order to improve machining performance and in the meantime reducing manufacturing cost. This study proposes sustainable machining process for this new generation Titanium Ti-5553 alloy. Process parameters including depth of cut, cutting speed, and feed rate were taken into account to optimize parameters for reducing tool wear, energy consumption, and surface roughness, and in the meantime increase material removal rate. Genetic algorithm was utilized for optimizing the process parameters. Obtained results illustrated that optimization using genetic algorithm is a very effective approach to substantially improve machining performance of this alloy and make machining process of this alloy more sustainable by reducing energy consumption, manufacturing cost and increasing material removal rate in machining process of new generation titanium alloy.