## 3 Results and Discussion
### Performance Appraisal of the Artificial Intelligence Techniques  
The GRG was predicted using hybrid models. The usefulness of the model was examined by comparing the predicted and the observed values. The purpose of using many physical and mathematical models is due to the important contribution offered by artificial intelligence and optimization techniques. Generally, physical-based and linear models are adopted for the prediction of the tribological behaviors of materials. These conventional techniques involve a lot of computational load, are time-consuming, and are unable to deal with the non-linearity intricacies of the tribological behaviors of materials such as PTFE matrix composites. By contrast, non-linear models based on AI can serve as a panacea to traditional models. The simulation process was performed in MATLAB 9.3 (R2020a). Figure 3 shows the bar plot of the raw data used as inputs to the models. The optimal structure of the SVR model was chosen via the trial-and-error approach. The model's prediction accuracies were appraised using the R\({}^{2}\), R, MSE, RMSE, and MAPE of Equations (15)-(19), respectively, in both the training and testing phases. Based upon the model inputs, the simulated outcomes in terms of the model appraisal metrics are depicted in Table 10. It was observed that the SVR model exhibited a poor training pattern following the appraisal measures. As a means of improving the training capability of the SVR model, optimization models, namely PSO and HHO, were added to the SVR model. Following the hybridization of the SVR model, its performance was found to increase. Moreover, when the hybrid models (SVR-PSO and SVR-HHO) are compared, different performances are observed (Table 10). Table 10 shows that the SVR-HHO model indicated the highest R\({}^{2}\) and R, but the lowest MSE, RMSE, and MAPE of 85%, 92%, 1%, 3%, and 3%, respectively, in predicting the GRG when compared with the SVR-PSO model. This shows that the model accurately learns the pattern of the data. Conclusively, the findings revealed that the predictive efficiency of the hybrid SVR-HHO model outshone that of the SVR-PSO and the SVR models for predicting the GRG. Additional investigation of the models was performed using radar plots, and is presented in Figure 19. It can be deciphered that SVR-HHO > SVR-PSO were adequate enough to capture the best fitting for the GRG (R\({}^{2}\) > 83%). Similarly, the numerical comparison of MSE showed that the SVR-HHO model with an MSE of 0.0014 is better than the SVR-PSO model with an MSE of 0.0031. The best predictive model reduced the error by 3%. Figure 20 showcases the scatter \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Optimal Parameter**} \\ \hline  & **Initial Design** & **Prediction** & **Validation** \\  & **Parameter** & & \\ \hline Level settings & L2G2 SD3 SS1 & L3G1 SD3 SS3 & L3G1 SD3 SS3 \\ GRG & 0.3988 & 0.8412 & 0.8311 \\ Enhancement (\%) & & 53 & 52 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of the validation test. plot between actual and predicted values of the SVR, SVR-PSO, and SVR-HHO models for GRG. The scatter plot gives an insight into the level of consistency between the predicted and actual GRG for the whole goodness of fit. It is clear from Figure 20 that the SVR-HHO model indicated better consistency between the actual and predicted GRG when compared with the SVR-PSO and SVR models.