## 3 Results and Discussion
The 3D surface representation is used to study the variation of the response with respect to two predicted parameters, while keeping the other two parameters at constant values (recommendation: maximum values). Figure 7 shows the 3D surface plots for the response GFRG with respect to two predicted GRC parameters, in this case GRCs for DA and KW. From this figure, it can be concluded that as GRC-DA and GRC-KW increase from a low to a high level, GFRG also increases. Mean GFRG parameters were calculated and shown in Table 3. The highest value of the mean GFRG response of the parameters was obtained when cutting a steel sheet with a thickness of 10 mm with LBM at the minimum cutting speed set on the machine. LBM was also selected for cutting a sheet with a thickness of 8 mm and 5 mm, but at a medium and maximum cutting speed, respectively. Although AWJM machining is often used for cutting steel in production, it proved to be the worst in this case due to the excessive machining time. To test the similarity between the means of the level of the main factors, such as the type of cutting process, a main effect diagram was used. This diagram shows the effects of the main factors and their interaction on GFR. In the main effects plot, it can be seen that the steep slope of the cutting process is the property that has the greatest influence on the estimation of GFR, Figure 8. By observing the slope of the curve, i.e., the mean values, an analysis with fuzzy 3D diagrams was confirmed. It is concluded that the presented processing conditions are the most suitable for LBM from the point of view of technological parameters. The same problem of selecting machine tools for non-conventional processes with multiple alternatives has also been solved by previous researchers [25; 26; 27]. Methods such  
Figure 8: Main effect plot of GFR. Figure 7: 3D surface plot of GFRG. as fuzzy--AHP or fuzzy--TOPSIS were mainly used. The results of the rankings mostly depended on the way the criteria were selected. The parameters from the machine tool catalog, such as accuracy, surface roughness, cost, etc., are mainly selected as criteria. Unlike that, experimentally determined values are used as criteria in this study. In summary, the advantage of the evaluation model developed in this study over the previously proposed method is that it can be very easily integrated into any decision-making system. Thus, the results of the decision do not have to be based only on the opinion of experts, but also on the actual performance of the machine tools included in the analysis. This helps to improve the overall performance of the observed production process. Due to the flexibility of fuzzy logic, the model obtained in this way can be additionally updated at any time with new data, new machine tools and the like. Moreover, the proposed model of a grey fuzzy approach shows its advantage in flexibility with respect to the preferences of the decision makers. A possible application of the hybrid method GFA proposed in this paper can be found in Industry 4.0, a concept that includes, among other things, automatic control to ensure the reliability and stability of the observed systems. In other words, the classical decision-making models are not adapted to the Industry 4.0 technology. Therefore, attention is paid to the application of artificial intelligence methods [40]. The grey relations approach is represented in a fuzzy system, which provides better flexibility of the model and better integration with intelligent decision systems. The development of the hybrid MCDM model enables the selection strategy of machine tools, and by integrating it into a more comprehensive system, it can be implemented in automatic control. Moreover, the complex imprecise and non-numerical information included in the proposed methodology can be improved with enhanced intelligence-oriented algorithms, which increases its authenticity and stability. The approach proposed in this paper can be applied to other problems where it is necessary to make correct decisions.