Parametric optimization to establish eco-friendly nanofluid minimum quantity lubrication (NMQL) practice for turning superalloy Inconel 718

Abstract

Purpose The purpose of this paper, an experimental study, is to investigate the optimal machining parameters for turning of nickel-based superalloy Inconel 718 under eco-friendly nanofluid minimum quantity lubrication (NMQL) environment to minimize cutting tool flank wear (Vb) and machined surface roughness (Ra). Design/methodology/approach The central composite rotatable design approach under response surface methodology (RSM) is adopted to prepare a design of experiments plan for conducting turning experiments. Findings The optimum value of input turning parameters: cutting speed (A), feed rate (B) and depth of cut (C) is found as 79.88 m/min, 0.1 mm/rev and 0.2 mm, respectively, with optimal output response parameters: Vb = 138.633 µm and Ra = 0.462 µm at the desirability level of 0.766. Feed rate: B and cutting speed: A2 are the leading model variables affecting Vb, with a percentage contribution rate of 12.06% and 43.69%, respectively, while cutting speed: A and feed rate: B are the significant factors for Ra, having a percentage contribution of 38.25% and 18.03%, respectively. Results of validation experiments confirm that the error between RSM predicted and experimental observed values for Vb and Ra is 3.28% and 3.75%, respectively, which is less than 5%, thus validating that the formed RSM models have a high degree of conformity with the obtained experimental results. Practical implications The outcomes of this research can be used as a reference machining database for various metal cutting industries to establish eco-friendly NMQL practices during the turning of superalloy Inconel 718 to enhance cutting tool performance and machined surface integrity. Originality/value No study has been communicated till now on the turning of Inconel 718 under NMQL conditions using olive oil blended with multi-walled carbon nanotubes-based nanofluid. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0317/