## 4 Discussion  
Accurately predicting wear rate and friction coefficient behavior in dental ceramics remains a critical challenge due to the complex interplay of mechanical loading, material microstructure, and surface treatments, necessitating advanced computational approaches to enhance predictive accuracy and material optimization. The findings of this study demonstrate that the application of genetic algorithm (GA) optimization significantly enhances the predictive accuracy of artificial neural networks (ANNs) for wear and friction estimation across different surface treatments of lithium disilicate, as evidenced by a reduced mean squared error (MSE) and strong correlations between actual and predicted values, as confirmed by Figures 8-10.  
In comparison to previous studies, our research relies on the application of a GA for fine-tuning ANN parameters in tribological testing, which significantly improved the model accuracy. Earlier studies did not use optimization techniques that could handle the complexities of tribological data, such as variations in normal force and sliding velocity, which often leads to overfitting and reduced generalization. Our application of the GA not only improved the performance in terms of reducing the MSE but also enabled the selection of relevant hyperparameters, further enhancing the model's robustness.  
Additionally, it is important to note how the results of this study fit into the broader context of research in tribology and dental material processing. Specifically, the increased accuracy in predicting wear and friction behavior enables better understanding and optimization of operational conditions for materials such as lithium disilicate. Our results suggest that parameters such as velocity and normal force are critical for prediction accuracy in tribological testing, aligning with previous research that showed similar findings regarding the interaction between these factors and material performance.  
While this study demonstrates the effectiveness of ANN models optimized using a genetic algorithm (GA) for predicting wear and friction in lithium disilicate ceramics, several limitations should be acknowledged. First, the dataset used for model training and validation is limited to specific experimental conditions, including predefined sliding velocities and normal loads. As a result, the generalization of the model to a wider range of operational conditions, such as varying humidity, temperature, or different lubrication environments, remains uncertain. Additionally, the study primarily focuses on three surface treatment methods--polished, glazed, and ground--without considering other clinically relevant finishing techniques or potential variations in surface roughness that may influence tribological behavior. Future research should explore additional surface treatments and include topographical analyses to enhance predictive accuracy.  
Finally, it is important to note that future studies should explore how different surface treatments affect long-term material performance in order to develop optimized strategies for longer lifespan and cost reduction in industrial applications. While our results are promising, additional research may contribute to further optimization and increase the applicability of this methodology to a wider range of tribological tests.