A hybrid algorithm for optimizing sustainable measures in MQL-turning of Inconel 800H

Inconel 800H superalloy is a recent and difficult-to-turn material. This research is focused on obtaining the best machining output effects, such as lesser cutting force, roughness, and residual stress by optimizing the input machining parameters such as cutting speed, feed rate, spraying angle, and nozzle distance using Nano fluid-based minimum quantity lubrication (MQL) a sustainable, cheaper, and environmentally safer method on Inconel 800H. The nanofluid employed in MQL has 0.20 wt % hBN+ remaining % of olive oil. Taguchi L27 is used for experimentation and the Multi-objective optimization (MOO) model of Harris Hawks optimizer (HHO) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is employed to determine the input optimal parameters. Models utilized five different weight schemes including the Analytic Hierarchy Process (AHP), Entropy weight method, Criteria Importance through Inter-Criteria Correlation (CRITIC), Grey relational analysis (GRA), Principal Component Analysis (PCA) to calculate response weights. AHP-TOPSIS model showed that the 70m/min of speed, 0.10 mm/rev of feed, 45° of nozzle angle, and 12mm of nozzle distance are the optimum MQL-turning parameters by accounting for all output responses. Significant improvement in MQL turning compared to dry turning in output responses of roughness, force, and residual stress 72.62%, 8.08%, and 19.32% respectively.