## 3 Results
### Optimization of ANN Hyperparameters Using Genetic Algorithm  
In this section, the results obtained from the application of a GA for optimizing the hyperparameters of ANNs trained on three types of processed lithium disilicate surfaces, polished, glazed, and ground, are presented. The optimization process was aimed at enhancing the prediction accuracy for the wear rate and coefficient of friction, which were influenced by varying parameters such as normal force and sliding velocity. The GA was applied to fine-tune the hyperparameters of the ANNs, which were trained on data obtained from tribological testing of the three types of surfaces. The optimization and training processes were conducted for each surface type, with specific time intervals for each process. Each training procedure lasted 50 generations, with a population size of 20 members per generation. The time durations for optimization and training were as follows: 1611.33 s for the polished surface, 1487.46 s for the glazed surface, and 1352.44 s for the ground surface. Figure 7 illustrates the progression of the mean squared error (MSE) values across iterations, highlighting the best and average values within each population. The graphs illustrate the convergence of the GA over 50 generations, depicting the penalty values for the best individual (black dots) and the mean penalty values across the entire population (blue dots) for each generation. The penalty values demonstrate a rapid decrease during the initial 10 generations for all three cases, indicating an efficient exploration of the solution space. Beyond the 15 th generation, the penalty values stabilize, signaling the algorithm's convergence toward an optimal solution. The best-achieved penalty MSE values are 0.000554227, 0.00027977, and 7.29834 \(\times\) 10\({}^{5}\) for the polished, glazed, and ground surface, respectively, while the mean MSE penalty value in the final generation is 0.00118152, 0.00330609, and 0.00539462 for the polished, glazed and ground surface, respectively. This discrepancy highlights the algorithm's capability to identify superior solutions within the population. The stabilization of the mean MSE penalty value across later generations confirms the population's adaptation to optimal regions of the search space. This observation underscores the robustness of the GA in solving complex optimization problems. The results validate the effectiveness of the implemented GA in minimizing the MSE penalty function. The rapid convergence and the achieved optimal penalty value reflect the algorithm's ability to optimize model parameters efficiently while maintaining population diversity during the initial stages of evolution. Table 5 presents the selected hyperparameters for the ANN models corresponding to the three types of processed lithium disilicate surfaces: polished, glazed, and ground. Figure 6: COF curve of IPS e.max CAD under polished surface (in the presence of artificial saliva and 10,000 fretting cycles): (**a**) 250 mN, 4 mm/s; (**b**) 1000 mN, 12 mm/s. The hyperparameter configurations for the ANN models corresponding to the polished, glazed, and ground surfaces of lithium disilicate material, as presented in Table 5, reveal several critical insights regarding the model architecture and training strategies employed. The number of hidden neurons varies slightly among the surfaces, reflecting differences in the data complexity for each type. The polished surface model, with 17 hidden