Optimizing Nanofluid Minimum Quantity Lubrication Machining of Inconel-800 Using Kriging Non-Dominated Sorting Genetic Algorithm II

This study optimizes the machining process of Inconel-800 superalloy using nanofluid minimum quantity lubrication (MQL) with multi-wall carbon nanotubes (MWCNTs) and biodegradable coconut oil. A Taguchi design with 27 trials is used to examine the effects of varying nanoparticle concentrations and machining parameters on surface roughness and temperature. The optimized nanofluid MQL system improves surface roughness by 26.22%, reduces surface roughness peak-to-valley by 12.06%, and significantly lowers temperature, demonstrating improved quality and thermal management. A Kriging model predicts outcomes with high accuracy (R2 > 0.9), and multi-objective optimization using Kriging and the non-dominated sorting genetic algorithm II identifies an optimal balance between surface roughness and temperature. Additionally, using coconut oil as the lubricant base in the nanofluid MQL system promotes sustainable machining by reducing reliance on conventional lubricants and environmental impact. These findings validate the effectiveness of advanced optimization techniques combined with nanofluid MQL for superior sustainable machining of superalloys.