## 5 Conclusions  
With a particular aspect of the CFRP composites machining technology, the lubrication and tool geometry were investigated in many studies. Based on the experimental and processing data, the lubrication cryogenic gas was the most remarkable attribute when observing quality criteria, such as the surface roughness, the uncut fiber, and the delamination caused during the drilling process.  
In this study, the TOPSIS optimization technique based on the GRC was proposed to validate the optimal parameter process. The closest coefficient value was used to specify the greatest parameters to achieve minimized surface roughness, uncut fiber, and delamination of the drilling hole. The optimal result was detected by L3T3F1 (cryogenic CO\({}_{2}\) lubrication, a high helix twist angle, feed rate of 228 mm/min) to obtain the greatest response, and a minimized surface roughness of 14.9567 \(\upmu\)m, no uncut fiber, and a delamination of 1.02 mm\({}^{2}\). ANOVA was used to confirm the distribution impact of the input parameters of the drilling process. The contributions of the lubrication, twist angle, and feed rate were 52.88%, 26.1%, and 8.43%, respectively. The analyzed results could be used to achieve the desired response quality under practical parameters in the drilling process.  
The results indicate that the proposed model is useful to facilitate the MCDM problem under an environment of uncertainty and vagueness. This relatively advanced approach is very effectual in rejecting process variation, and is a great assistive strategy compared to other MCDM approaches. Each response has been weighted and diminishes the versatility of the process of decision-making (DM).  
The productiveness of the method could reach further capacity, and could be applied with a large quantity of multi-criteria inputs and response outputs. The experimental method could be enlarged to other drilling parameter processes and evaluations. Furthermore, this optimization method could be meaningfully and beneficially applied to other machining technologies.  
Conceptualization, Q.-P.T. and S.-C.H.; methodology, Q.-P.T. and S.-C.H.; software, Q.-P.T., V.-N.N.; validation, Q.-P.T. and S.-C.H.; formal analysis, S.-C.H.; investigation, Q.-P.T.; data curation, Q.-P.T., V.-N.N.; writing--original draft preparation, Q.-P.T., V.-N.N.; writing--review and editing, Q.-P.T. and S.-C.H.; supervision, S.-C.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministry of Science and Technology of the Republic of China MOST 109-2622-E-992-008-CC3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Initial**} & \multicolumn{3}{c}{**Optimal Parameter**} \\ \cline{2-6}  & \multicolumn{3}{c}{**GRA**} & \multicolumn{3}{c}{**Grey-TOPSIS**} \\ \cline{2-6}  & & **Prediction** & **Experimental** & **Prediction** & **Experimental** \\ \hline Level & L1, T3, F2 & L3, T3, F1 & L3, T3, F1 & L3, T3, F1 & L3, T3, F1 \\ \hline The Surface & 15.7477 & & 14.9567 & & 14.9567 \\ Roughness & 0.03 & & 0 & 0 \\ The Uncut Fiber & 1.21 & & 1.02 & 1.02 \\ \(\mathrm{C_{u}}\) & 0.3666 & & 1 & 0.9989 \\ GRG & 0.689 & 0.95 & 0.92 & & \\ Improvement & & 0.261 & 0.231 & 0.6334 & 0.6323 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Initial, predicted, and compared evaluation test.