Optimizing sustainable machining processes: a comparative study of multi-objective optimization techniques for minimum quantity lubrication with natural material derivatives in turning SS304

Chromium-nickel based stainless steel is being used widely in various industrial applications due to outstanding benefits such as fatigue, thermal, and corrosion resistance. However, because of the significant heat generated during the cutting process, these alloys are challenging to machine. Flood Lubrication eliminates this problem but has negative effects on the environment and public health. For this reason, Minimum Quantity Lubrication is preferred. The performance can be further increased with use the use of natural materials derivatives with the MQL system. The current study aims to investigate the influence of process parameters and to optimize the process parameters using TOPSIS and Grey Relational Analysis (GRA). A Taguchi method is used to design the four-factor 3-level based L₂₇ orthogonal array with process variables such as lubrication environment (LE), speed (Vc), feed (f), and depth of cut (a). ANOVA is performed to identify the influence of each parameter such as cutting temperature (CT), surface roughness (SR), and cutting tool vibration (TV). The study revealed that the lubrication environment have the highest influence over the overall performance characteristics followed by speed and depth of cut. The optimal set of parameters for SS304 turning using corn-oil-based MQL has demonstrated an improvement in preference values for TOPSIS and GRA of 0.78 and 0.61, respectively. The parameter set recommended by TOPSIS is validated using a confirmatory test. Microstructure analysis after the turning operation was performed and revealed the development of high-quality finishing from the corn oil-based MQL.