## 5 Conclusions  
The current investigation presents the synthesis and application of LRT-30 oil-based ZnO nano-cutting lubricant application in the hard turning of AISI 52100 bearing steel using dual-nozzle-injecting MQL system. The responses like tool wear, surface roughness, power consumption, cutting temperature, and cutting noise are measured and analyzed with respect to cutting inputs' variables. The following key findings are observed:  
* The tool-wear was greatly controlled using ZnO nano-cutting lubricant due to the better lubricating characteristics of the synthesized fluid. The effects of cutting speed were traced to be the largest (76.68%), followed by flow rate (11.94%), depth of cut (6.27%), and feed (4.94%). The mechanisms like abrasion, chipping, and diffusion were found to be dominant. * The surface roughness (Ra) was extensively influenced by feed (74.26%) and depth of cut (15.88%). The impact of flow rate over Ra was traced to be insignificant. * The power consumption observed throughout the machining process showed an increase in conjunction with elevated cutting speeds and flow rates. Significant impacts of the feed-depth of cut, feed-flow rate, and depth of cut-flow rate interactions were also found for power consumption. * The cutting temperature was greatly reduced with use of dual-nozzle nano-cutting lubricant. All the input variables had a significant impact on cutting temperature. The cutting speed was the largest consequence (57.25%) on cutting temperature, followed by flow rate (19.22%), depth of cut (16.73%), and feed (6.4%). * The cutting noise in the entire experiment was found to be in the range of 76.08 to 83.31 dB, which is relatively lower than the human hearing capability (85 dB). The effects of the depth of cut were determined to be the largest (33%) while other inputs were also significant. In summary, ZnO-based nano-cutting fluid has great ability to retard growth in wear, surface roughness, and power consumption. In future research, different nozzle positions and nano-fluid concentrations can be used for machining this bearing steel. Also, uni-variate experiments can be conducted to study the effects of single factors on cutting performance. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app131810062/s1](https://www.mdpi.com/article/10.3390/app131810062/s1)]([https://www.mdpi.com/article/10.3390/app131810062/s1](https://www.mdpi.com/article/10.3390/app131810062/s1)). Table S1. Normalization. Table S2. Calculation for determining Pij and Eij. Table S3. Calculated values of normalized entropy Eij and weight (Wij). Table S4. Total Relative Importance values based on WSM. Table S5. Total Relative Importance values based on WPM. \begin{table}
\begin{tabular}{c c c} \hline
**Experiment No.** & **Cumulative Relative Relevance Index** & **Rank** \\ \hline
1 & 0.603162 & 4 \\
2 & 0.605453 & 3 \\
3 & 0.427278 & 7 \\
4 & 0.295346 & 13 \\
5 & 0.485798 & 5 \\
6 & 0.768964 & 2 \\
7 & 0.25192 & 16 \\
8 & 0.293974 & 14 \\
9 & 0.769163 & 1 \\
10 & 0.37983 & 9 \\
11 & 0.483902 & 6 \\
12 & 0.352656 & 11 \\
13 & 0.287809 & 15 \\
14 & 0.348862 & 12 \\
15 & 0.396269 & 8 \\
16 & 0.364621 & 10 \\ \hline \end{tabular}
\end{table} 
Table 7: Ranking and Cumulative Relative Relevance Index obtained by WASPS optimization. **Author Contributions:** Abstract, S.K. and R.K.; Introduction, A.P. and R.K.; Methodology implementation, S.K., A.K.S., R.K. and A.P. ; Experimentation, S.K. and R.K.; Results and Discussion, S.K., A.P. and R.K.; Writing--review and editing, A.K.S. and R.K.; Supervision, R.K. and A.K.S. ; Optimization, S.K., A.P. and R.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This study received no financial support. The APC was paid using the reviewer vouchers that the authors had on hand. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable.