Parametric analysis of MQL-based sustainable machining using nano alumina mixed coconut oil-based cutting fluid: a computing approach

To explore the aerospace, chemical, and cryogenic vessel sectors applications, this work computes the minimum quantity lubrication using nano alumina mixed coconut oil-based cutting fluid for the machining parametric analysis on stainless steel 304 substrate applications in global sustainable development. Therefore, the authors attempt to vary the weight percentages of nano aluminum oxide particles added with coconut oil to enhance the machined surface characteristics. The analysis of input machining parameters such as cutting fluid (CF), spindle speed (SD), feed rate (FD), and depth of cut (DC) are investigated on the output responses i.e., material removal rate (MRR), and surface roughness (SR). The machining experiments are conducted with help of Taguchi methodology for the design of experiments by L-16 orthogonal array. During designing, optimization is carried out by using Minitab computer software, for four different combinations of response variables considering the different importance of MRR and SR under various sustainable manufacturing circumstances. For optimization of these output responses during the machining process, the desirability function approach is used to attain better machining performance. Finally, it is revealed that the optimum parametric combination for better machining performance is to be obtained by CF4 (coconut oil + 6% wt of Al2O3 nano-powder), SD = 1600 rpm, FD = 0.18 mm/rev, and DC = 2 mm. It is reported that the depth of cut is identified to be the highest important parameter with a contribution of 48.27% for composite desirability. The predictability of the regression model is found to be more than 95% for all the responses which highlight model significance.