## 4 Discussion
\(\mathrm{T_{s}}\) reduction due to the increase in the sliding distance is attributed to the pull out or fracture of the abrasives as a result of the presence of tough carbon and bronze fibers. Additionally, wear debris is transferred to the counterface from the PTFE, causing reduced abrasion. Lower \(\mathrm{T_{s}}\) due to increasing sliding is because the increase in speed increases the momentum transfer in the perpendicular direction, thus increasing the upward force upon the sample surface due to rotational inertia. Additionally, the travelling time is depreciated due to the increasing sliding speed. This minimizes wear debris generation, causing a reduced wear rate. Besides, at higher sliding speeds, plastic deformation becomes more difficult, causing less extensive damage compared to lower sliding speeds. Moreover, abrasive particle cracking increased at higher speeds, thereby decreasing the cutting efficiency of the abrasives and blunting the sharp edges of the abrasive particles. The results of the single and multi-response optimization and ANOVA revealed different behaviors, implying that different tribological mechanisms might have been involved. In both the optimizations and ANOVA, grit size was the most influential parameter. This is attributed to the maximum inclination of the grit size compared to the load, distance, and speed. Similar results were obtained by [PERSON] [22]. From the confirmatory test, it was found that there was performance enhancement (52%) in the GRG over the initial design parameters. This improvement is explained by the fact that the GRA has added its sterling optimization features to those of the Taguchi technique, which cannot deal with multiple outcomes simultaneously. This confirms the validity of the Taguchi technique coupled with GRA for studying the abrasive tribological performance of PTFE matrix composites. With respect to the prediction ability of the SVR, SVR-PSO, and SVR-HHO models, it was found that the SVR model was not good enough at calibrating and verifying the GRG of the PTFE matrix composites. The reason for the poor performance of the SVR model compared to its hybrids might be due to the highly complex and nonlinear nature of the data. Fundamentally, it should be considered that the promising prediction occurred in the course of the training phase, which is originally used to precisely measure the model based on known inputs and outputs. Nevertheless, the verification stage is significant in appraising the efficacy of the models, since it closely inspects the models' prediction efficiency based upon unknown magnitudes. This advantage is not enjoyed by the training phase of the SVR model. Consequently, a robust model should possess a determinate and balanced performance in both its training and testing stages. In general, the optimization algorithms showed promising ability when compared with a single model. The exceptional performance of the hybrid models is attributed to their ability to reduce errors and complexities. The slightly higher performance of the SVR-HHO model in comparison to the SVR-PSO model related to its cooperative mechanism and hunting behavior. Furthermore, the R values of the models were determined to be >80%, supporting the conclusions drawn by [38; 48; 49] that values >70% are considered acceptable. Consequently, the outcomes of all of the optimization models are acceptable (Table 10). The predictive superiority of the HHO model over the others is in concordance with the results obtained by [50]. In spite of the availability of ample metaheuristic models, there is no particular model that can guarantee consistent optimal performance in addressing various types of issues. Yet, the latest research on unique population-based and nature-inspired optimization paradigm (HHO) models indicated better adequacy in establishing optimal solutions to problems in multi-objective forms.