Machining performance optimisation of MQL-assisted turning of Inconel-825 superalloy using GA for industrial applications

Abstract

This work investigates machining performance of Inconel 825 using chemical vapour deposition (CVD) TiN coated inserts in minimum quantity lubrication (MQL) approach. Three important measures in relation to machining of hard-to-cut materials viz., surface roughness (R a), tool wear (VB), and cutting temperature (CT) have been considered for investigation. The response surface methodology (RSM) modelling and relationship between input and output factors is studied. The machining parameters are optimised individually as well as simultaneously using genetic algorithm (GA). The minimum R a of 0.39 µm, VB of 15.37 µm and CT of 56.47°C was obtained. The significant contribution of this research is that the values of R a , VB and CT obtained are quite low when compared to those reported in literatures. For the application in manufacturing industries, a technology table is generated for selection of optimum process parameters having minimum VB or minimum CT satisfying the desired value of surface finish of components produced.