Multi-Objective Optimization During Machining Ti-6Al-4V Using Nano-Fluids
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Several properties make titanium and its alloy the primary candidate to attain weight and functional advantages because of its promising properties such as high strength to weight ratio, high corrosion resistivity, and high yield stress. Although titanium alloys have superior properties, some inherent characteristics such as high chemical reactivity and low thermal conductivity lead to poor machinability and result in premature tool failure and shortened tool life. In order to overcome the heat dissipation challenge during machining of titanium alloys, nano-cutting fluids are utilized as they offer higher observed thermal conductivity values compared to the base oil. Thus, in the current work, multi-walled-carbon nanotubes (MWCNTs) cutting fluids along with minimum quantity lubrication (MQL) have been employed during machining Ti-6Al-4V. On the other hand, developing a multi-objective optimization model for machining titanium alloys is a promising step in order to minimize machining cost, achieve excellent surface quality, and increase the cutting tool life by selecting the optimal cutting conditions (i.e. cutting speed, feed rate, depth of cut). In this study, response surface methodology (RSM), and genetic algorithm (GA) are employed to model and optimize three main machining responses: tool wear, surface quality, and power consumption. Three main independent processes parameters are considered when machining titanium alloys, namely; cutting speed, feed rate, and percentage of added nano-additives.