A hybrid algorithm for multi-objective optimisation of parameters nanofluid in MQL-turning of Inconel X-750

Abstract

ABSTRACT Inconel X-750 is referred to as hard-to-turn material in conventional machining due to the excellent mechanical and thermal properties. The present study focused on the applicability of minimum quantity of lubrication enhanced with nanoparticles to machine Inconel X-750. The nanofluid for MQL turning is 0.25 wt.% silver (Ag) + coconut oil. The process improvement of MQL is greatly dependent on MQL and machining parameters. In the L27 turning experiments, the MQL-turning parameters used were nozzle angle and distance, cutting speed, and feed rate, and the output parameters were residual stress, cutting force, and roughness. For optimisation, the Multi-Objective Golden Eagle Optimisation (MOGEO,a meta-heuristic algorithm) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are utilised. Analytical hierarchy processes are utilised for weight calculation in optimisation and their effectiveness was compared. From the experimental results, the nozzle angle and distance are more significant factors for surface finish and residual stress than the force. The improvement of roughness by 6.83%, force by 47.13% and residual stress by 51.70% is observed at the optimal determined values by MOGEA rather than that of TOPSIS. The proposed hybrid algorithm of weighted MOGEO can be applied to other machining applications.