Eco-Friendly Machining of Ti-6Al-4V Alloy: Optimization Using a Hybrid Algorithm (Grey Wolf Algorithm and VIKOR Algorithm)

Machining quality and performance of Ti-6Al-4V is studied and analyzed in terms of surface roughness, workpiece vibrations (viz. measured in terms of displacement amplitude) and tool wear while plain turning operation on CNC turn-mill machine tool under mist cooled lubricant application with cutting velocities 80–200 m/min. Due to low yield stress and high hardness, the titanium alloy has a spring back effect which induces workpiece vibrations, roughness on the machined workpiece and thereby wear out the cutting tool. Regression empirical models using response surface methodology on the objectives are developed, and ANOVA is used to test adequacy and identification of significant input parameters. The multi-objective Grey wolf optimization (MOGWO) technique is employed to find optimal set of input parameters and thereby this obtained data of Pareto solutions (set of input parameters) are used to determine the optimal solution using the multi-criteria decision-making (like VIKOR) method. The empirical models developed were significant with more than 0.81 goodness of fit. The tool wear observed to be in a state of decreasing, when machined in the lower range of the design (80–140 m/min), while seemed to be increase thereon. The optimum input parameter set obtained using hybrid technique (MOGWO coupled with VIKOR) is 105.585 m/min cutting velocity, 0.1 mm/rev tool feed and 0.346 mm depth of cut, giving rise of objective values as 0.695 µm surface roughness, 145.752 μm tool wear and 26.711 µm workpiece vibrations.