Multi-objective optimization on turning of additively manufactured Inconel 625 alloy using grey relational analysis

ABSTRACT The unique properties of Inconel 625 make it desirable for engineering applications. It is expensive and complex to machine Inconel 625 due to its unique properties. In light of the widespread use of turned components in crucial aircraft engines, the turning method was chosen to evaluate the impact of turning parameters on cutting forces, surface roughness, material removal rate and temperature. In this study, the Taguchi optimization approach is applied to optimize cutting parameters with laser textured tungsten carbide with HBN nano particle filled cutting tools during high-speed turning of Inconel 625. The cutting parameters include the depth of cut, feed rate, and speed at which the work piece is turned. ANOVA was used to identify the most influencing process parameters on the turning operation. The cutting speed of 14 m/min, Feed rate of 0.3 mm/rev and depth of cut of 0.33 mm gave a better cutting force of 239 N and material removal rate of 3.22 mm3/min and also a reduced surface roughness to 2.834 µm, and temperature of 61°C. From ANOVA, feed rate 77.38% was identified the most influencing process parameter on the turning operation followed by cutting speed 8.65% and depth of cut 0.41%.