## 1 Introduction
Additionally, studies on polyphenylene sulfide (PPS) composites have demonstrated that optimizing ANN architectures and learning parameters improves predictive accuracy and ensures robustness across various testing conditions [10]. While other machine learning (ML) techniques, such as k-nearest neighbors (KNN) and gradient boosting algorithms like XGBoost, have been successfully applied to similar studies, ANNs remain underutilized for predicting the tribological characteristics of dental ceramics. Given the complexity of interactions between the ceramic microstructure, mechanical loading, and wear mechanisms, ANNs present a promising tool for capturing nonlinear dependencies that traditional statistical models may overlook. Recent studies have started employing ANNs in a tribological analysis of advanced coatings, reinforcing the potential of these models for predicting material behavior under different operating conditions [11]. Despite the widespread application of ANNs in tribology, its use in dental material research remains relatively limited. While ML techniques have been employed in predicting the mechanical properties of dental composites, such as tensile strength and bond durability, the modeling of tribological behavior, including wear resistance and friction coefficients, has not been extensively investigated. Some studies have explored the use of ANNs in dental applications, such as predicting the mechanical performance of adhesivesand composite formulations [12]. Recent research has also applied ANN models to assess wear behavior in dental composites, demonstrating their potential in identifying the key material and environmental factors that influence tribological properties [13]. This study aims to bridge this research gap by utilizing ANNs to model and predict the tribological performance of lithium disilicate dental ceramics under various testing conditions, ultimately contributing to the advancement of computational intelligence in dental tribology. In recent decades, advancements in dental materials have revolutionized dentistry, particularly with the introduction of metal-free ceramics. These materials combine durability and aesthetics, enabling restorations that closely mimic natural teeth. Modern ceramic systems offer exceptional strength and aesthetics, making them essential in contemporary practice. Among these, lithium disilicate ceramics stand out for their superior properties, redefining the standards of modern dentistry. Lithium disilicate ceramics have become the gold standard in metal-free dental restorations due to their superior aesthetic and mechanical properties. This material is characterized by a high crystal content (-70%), which provides exceptional flexural strength and wear resistance, making it ideal for crowns, inlays, onlays, and veneers [14]. In the past decade, research has focused on optimizing the microstructure of lithium disilicate ceramics. High-Density Micromization (HDM) technology enables uniform crystal dispersion, resulting in enhanced fracture resistance and aesthetics [15]. Additionally, innovative manufacturing techniques, such as 3D printing [16], have gained attention for their potential to improve ceramic processing and material performance, further expanding the possibilities for dental applications. This technology allows for flexural strength up to 500 MPa, with minimal risk of wear to opposing teeth [17]. Based on previous research and the identified research gap, this study tests the null hypothesis that there is a significant correlation between experimental parameters, such as normal load and the sliding velocity, and the predicted values of the wear rate and friction coefficient using ANNs. Additionally, it examines whether ANN models optimized with a genetic algorithm (GA) manage to improve the prediction accuracy compared to traditional methods for analyzing tribological data.