## 4 Conclusions  
In this research, the impact of the cutting speed \(v_{c}\), feed rate \(f\), depth of cut \(a_{p}\), and insert radius \(r_{i}\) on surface roughness \(R_{a}\), main cutting force \(F_{c}\), passive force \(F_{p}\), and feed force \(F_{f}\) for both types of inserts was assessed. Three cases were examined, where in case 1, data from an insert with a radius of 0.4 were used, while in the second case, data from an insert with a radius of 0.8 were used. The first and second cases used cutting speed \(v_{c}\), feed rate \(f\), and depth of cut \(a_{p}\) as the input variables, while average surface roughness \(R_{a}\), main cutting force \(F_{c}\), passive force \(F_{p}\), and feed force \(F_{f}\) were the output variables. In the third case, both types of insert data were combined; therefore, the \(r_{i}\) input variable was used as an additional input variable. For each case, a neural network model was developed, followed by SHAP analysis. In addition, multi-criteria PSI-TOPSIS analysis was conducted, and the results were compared. The following conclusions can be drawn:  
* The developed neural network models showed high performances for all variables in all three cases, except for \(R_{a}\) in cases 1 and 2 and for \(F_{p}\) in case 2. The values of \(R^{2}\) for all variables in cases 1 and 2, except for \(R_{a}\) and \(F_{p}\), were high, while in case 3, the variabilities of all output variables were explained by input variables with high values of \(R^{2}\). * The proposed PSI-TOPSIS technique proved to be a very successful tool for determining the optimal combination of input parameters in the turning process for 90 MnCrV7 steel, regardless of the number of considered alternatives. * According to the SHAP analysis, feed rate \(f\) and depth of cut \(a_{p}\) showed the highest impacts on all output variables \(R_{a}\), \(F_{c}\), \(F_{p}\), and \(F_{f}\), where \(a_{p}\) was the variable with the highest overall impact in cases 1 and 2. In case 3, the SHAP analysis showed that  
Figure 19: Main effects plots for the mean output responses: (**a**) Surface roughness; (**b**) Main cutting force; (**c**) Passive force; (**d**) Feed force. when insert radius \(r_{i}\) was added, it had the highest impact on \(R_{a}\) and \(F_{p}\), while \(a_{p}\) had the highest impact on \(F_{c}\) and \(F_{f}\). The cutting speed \(v_{c}\) consistently showed the lowest impact in all three cases. * According to the multi-criteria analysis, depth of cut was the most important variable for all outputs, and the results aligned with those of the SHAP analysis, where \(a_{p}\) was the variable with the highest overall impact. * The performances of models were low in case 1 and case 2 for \(R_{a}\) and in case 2 for \(F_{p}\). In case 3, with the inclusion of \(r_{i}\), the performance of the model was higher for \(R_{a}\) and \(F_{p}\), and with the SHAP analysis showing the highest impact of \(r_{i}\) on these two variables, it can be concluded that the insert radius is an important input variable to be considered. * The results showed that an insert radius of 0.8 mm, a cutting speed of 260 m/min, a feed rate of 0.08 mm, and a depth of cut of 0.5 mm were the optimal combination of input parameters. * Future research could include analysis of the impact of turning parameters on process performance, such as specific energy and tool wear, with different materials. Conceptualization, M.P., D.M., D.B.-H., A.Z., M.M. and K.M. ; Methodology, M.P., D.L., D.B.-H., A.Z. and M.M. ; Software, M.P. and D.B.-H.; Validation, M.P., D.L., D.B.-H., A.Z., M.M. and K.M. ; Formal analysis, M.P., D.M., D.B.-H. and K.M. ; Investigation, M.P., D.M., D.L., D.B.-H., A.Z., M.M. and K.M. ; Resources, D.M., D.L., A.Z. and M.M. ; Data curation, M.P., D.M., D.B.-H. and K.M. ; Writing--original draft, M.P., D.M., D.L., D.B.-H., A.Z., M.M. and K.M. ; Writing--review & editing, M.P., D.L., D.B.-H., A.Z., M.M. and K.M. ; Visualization, M.P., A.Z. and M.M. ; Supervision, D.L., D.B.-H., A.Z. and M.M.