## 1 Introduction  
The study of tribological properties--friction, wear, and lubrication--is essential for understanding material performance in applications where mechanical contact is a critical factor. These properties directly influence the reliability, efficiency, and longevity of components in industries such as automotive, aerospace, and biomedical engineering [1]. Traditional methods for evaluating tribological properties rely primarily on experimental testing, which, while accurate, can be resource intensive, time consuming, and sometimes inconsistent due to environmental variability [2, 3]. Some of the most widely used techniques include pin-on-disk testing, which involves a stationary pin loaded against a rotating disk, allowing researchers to analyze the wear rate and friction coefficient under controlled conditions. However, this method may not fully replicate real-world dynamic interactions between materials. Reciprocating tribometers is used to simulate an oscillatory sliding motion, which is particularly relevant in dental applications where mastication induces cyclic contact between surfaces. Micro- and nanoindentation are used to measure hardness,elasticity, and wear resistance at micro- and nanoscale levels. While highly precise, they may not provide comprehensive tribological insights for bulk material behavior; Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM) are essential for examining worn surfaces, detecting surface roughness, and identifying wear mechanisms such as adhesion, abrasion, and fatigue wear. However, these methods are post-mortem analyses, meaning they do not allow for the real-time observation of wear progression [2; 4; 5]. Despite their precision, these traditional approaches face significant limitations. They often require extensive testing cycles, costly instrumentation, and complex data interpretation. Additionally, they may not adequately capture the multifactorial and nonlinear interactions between material composition, contact pressure, sliding velocity, and environmental conditions, all of which influence wear and friction behavior [1]. To overcome the limitations of traditional experimental methods, researchers have increasingly turned to computational and artificial-intelligence-based models to enhance material characterization. These approaches reduce testing time, improve predictive accuracy, and optimize material selection processes in tribology research. Among these methods, artificial neural networks (ANNs) have gained prominence due to their ability to model complex, nonlinear relationships between input parameters (e.g., load, velocity, and material composition) and tribological properties (e.g., wear rate and friction coefficient). Their adaptability and capacity to process large datasets have led to their widespread adoption in materials science, where they facilitate predictive modeling, the optimization of material properties, and a reduced need for exhaustive experimental studies. The application of ANNs in tribological research spans multiple domains, including composite materials, polymer-based systems, and biomedical applications. In composite materials, ANN models have been successfully used to predict wear and friction behavior in fiber-reinforced composites by incorporating variables such as fiber orientation, applied load, and sliding velocity, demonstrating high accuracy in capturing complex material interactions [6]. Similarly, ANNs have shown potential in optimizing surface treatment techniques, such as plasma nitriding, where predictive models estimate wear resistance based on process parameters like treatment temperature and duration [7]. In polymer-based tribological systems, ANNs have proven effective in modeling the wear behavior of polymer composites, including carbon-fiber-reinforced materials. By analyzing key tribological parameters such as contact pressure, temperature, and mechanical load, ANN-based models have successfully predicted wear loss and friction coefficients with remarkable precision [8; 9].