Evaluation of nano-MoS2-assisted MQL performances in end milling of AISI 316L austenitic stainless steel and multi-objective optimization via genetic algorithm

Abstract

Recently, eco-friendly machining technologies have emerged in compliance with the green manufacturing trend to mitigate the excessive use of conventional cutting fluids, limiting their adverse impact on the environment and worker's health. In this context, minimum quantity lubrication (MQL) has shown efficacy in dealing with this issue. Moreover, nanofluid-assisted MQL (N-MQL) has been proposed as an advanced technique to further improve the MQL performance, particularly in machining difficult-to-cut materials such as stainless steel. Therefore, this study aims to improve the machining performance during the end milling of AISI 316L stainless steel under several cutting conditions, including dry, MQL, and Molybdenum disulfide nanoparticles (MoS2)-assisted MQL conditions, focusing on surface roughness (Ra), main cutting force (Fc), and cutting temperature (T). This study showed that pure MQL and N-MQL outperformed dry conditions; the results indicated that Ra, Fc, and T were reduced under pure MQL by 26.82%, 12.13%, and 18.61%, respectively, and by 41.16%, 18.17%, and 25.27% with N-MQL. Finally, statistical analysis, regression modeling, and multi-objective optimization via the genetic algorithm (GA) approach were performed.