Performance Optimization of Ti-6Al-4V Milling Process Using Sustainable Cooling Approach and Application of Rao Algorithms

Abstract

Titanium alloy is the most promising superalloy widely used in avionics systems, because of its high strength and great corrosion resistance. Machining efficiency of this alloy can be enhanced with modeling and optimization approaches. In the present work, modeling of response surface methodology is used for milling of Ti-6Al-4V using no-coolant (dry), minimum amount coolant (MAC), and use of liquid carbon dioxide as cryogen with spray of nitrogen gas besides minimum amount of biodegradable mixture (Hybrid). Cutting speed (v), feed rate (f), and depth of cut (d) are input parameters, whereas temperature, tool wear, surface finish, and material removal rate are responses. GA, JAYA, and Rao1, 2, and 3 algorithms were used to solve a multi-objective function (Z). For hybrid condition compared to no-coolant and MAC, 35 and 17% reduction in T, 32 and 21% reduction in V_f, 45 and 33% reduction in R_a, and 45 and 15% improvement in MRR, respectively, was observed. JAYA performed 91, 64, 62, and 57% better than GA, Rao-1, Rao-2, and Rao-3 algorithms, respectively, for MAC condition. It has been observed that JAYA algorithm is better at achieving steady state and requires less generations, whereas Rao algorithms run faster. From the computational tests, it is observed that the performance of the Rao algorithm is superior to the other optimization algorithms. In terms of execution time, Rao-1 performed 49, 74, and 42% better than Rao-3, whereas 60, 77, and 41% better when compared to Rao-2 for three milling conditions, respectively.

Graphical Abstract