Optimization of Multiple Objectives by Genetic Algorithm for Turning of AISI 1040 Steel Using Al2O3 Nano Fluid with MQL

Abstract

Multi-objective optimization requires computing the best trade-off between two or more conflicting objectives. The present study applies multi-objective optimization technique to the turning of AISI 1040 steel using Al2O3 nano particles with minimum quantity lubrication (MQL) technique for cutting force (CF), surface roughness (SR) and temperature (CT) using genetic algorithm. Central composite face-centered design with five factors, namely volume concentration(vol.c) of nano particles, MQL flow rate, cutting speed, feed rate and depth of cut (DOC) at three levels are used for experiments. From the developed regression model, it is found that speed, feed and DOC are the primary factors effecting the CF whereas MQL flow rate, speed and DOC are the primary factors effecting the SR and cutting temperature is predominantly affected by MQL flow rate, speed, feed rate and DOC. Based on the mathematical models, multi-objective optimization of process parameters has been performed with genetic algorithm (GA) technique. A set of confirmation experiments were conducted for randomly selected trials of pareto solutions obtained from multi-objective GA to validate the optimum values. An error percentage of 4.6%, 3.7% and 4.9% respectively for CF, SR and CT shows that the predicted optimum values are justified with the confirmation result.

Keywords

• ccf
• genetic algorithm
• mql
• modeling
• multi-objective
• nano cutting fluids
• optimization
• turning