Optimizing the Process Parameters for Eco-Friendly Minimum Quantity Lubrication-Turning of AISI 4340 Alloy with Nano-Lubricants Using a Grey Wolf Optimization Approach - Chukwuka Prosper Ozule; Sunday Ayoola Oke; Ugochukwu Sixtus Nwankiti (Program Pascasarjana Magister Teknik Industri Universitas Mercu Buana, 2022)
Optimization of turning process parameters in minimum quantity lubrication (MQL)-assisted mode is obligatory for enhanced efficiency and product integrity. However, little attention has been paid to analyzing situations where high search precision is needed when evaluating the optimal turning process parameters. This article applies the grey wolf optimization (GWO) approach to optimize the turning of parameters AISI 4340 alloy to enhance cutting force, surface roughness and tool wear. Based on the literature data, turning was conducted with MQL-assisted CuO and Al2O3 nanofluids. The problem was formulated by mimicking six wolves in six different objective functions. The objective functions have the responses as the dependent variables and the parameters including cutting speed, feed and cutting depth as independent variables. The hunting behavior of the wolves as they encircle the prey is interpreted to the machining task optimization. It involves three hierarchically-evaluated guides- the alpha, beta and delta wolves- positioned optimally and other wolves are updated accordingly. The cutting speed, feed and cutting depth are bound in the lower and upper limits as 80 and 140m/min, 0.05 and 0.20m/m/rev and 0.1 and 0.4mm, respectively. The grey wolf optimization algorithm optimizes the parameters to yield the cutting force, surface roughness and tool wear using Al2O3 as 199.50N, -23.54mm and 0.06mm, respectively. For the CuO, the corresponding cutting force, surface roughness and tool wear, the CuO, Al2O3 and CuO nano lubricants produced the best results. However, for mass production, selective use of CuO and Al2O3 should be made. The usefulness of this research endeavor is to help process engineers to make decisions in producing low-cost components in manufacturing.