## 1 Introduction
By using ring-shaped blanks and conducting compression and hardness tests, the study shows the significance of maintaining material hardness and compressive strength, especially at high temperatures. Some studies also demonstrate the importance of optimizing operating parameters to reduce wear on cutter discs. [PERSON] et al. [6] and [PERSON] et al. [7] investigated the relationship between the rotational speed of a TBM's cutter head and the position of its disc cutters and their wear. As discussed by these authors [6; 7], it is crucial to control factors such as torque, thrust force, advance rate, and rotation speed to extend the service life of a disc cutter and reduce maintenance. In addition, advances in models for estimating the wear of disc cutters, as discussed by [PERSON] et al. [6], provide crucial insights for accurately assessing the operational behavior of disc cutters under different geological conditions. Understanding the forces acting on rock during cutting, as studied by [PERSON] [8], also contributes to improving the design of disc cutters. Predictive models also include various computer simulations and machine learning methods to estimate the wear of disc cutters. Finite element analyses (FEAs) and the discrete element method (DEM) are commonly used to predict the wear of disc cutters under different operating and geological conditions. [PERSON] and [PERSON] [9] and [PERSON] and [PERSON] [10] used the DEM and particle flow code (PFC3D) to analyze the behavior of disc cutters and demonstrate their effectiveness in predicting wear patterns and failure aspects. They showed that a DEM-based simulation can better represent microcracks in rock material. [PERSON] et al. [11] used the DEM to analyze the effects of disc cutter features, including tip angles and tip widths, on wear. Their results indicated that the tip angle and penetration depth have a direct effect on cutter wear. [PERSON] et al. [12] used the PFC3D to simulate the evolution of cutter chipping. They found that the cutting speed and an increase in penetration depth increased the probability and intensity of cutter chipping. The presence of initial defects also had a negative effect on the service life of the disc cutter. It should be noted that numerical modeling is a complex process that requires experience in computational methods, material characterization, and wear physics. Validating numerical models with experimental data is crucial to ensuring the accuracy and reliability of predictions. Obtaining the data required for numerical modeling can also be time-consuming and costly. This includes obtaining detailed material properties for the rock and disc cutter and conducting experimental laboratory tests and extensive data analysis. In addition, the exact tunneling parameters must be taken into account. All these factors should be considered when implementing numerical modeling as a tool for wear analysis as they may affect the overall feasibility and practicality of the approach. In contrast to numerical modeling, which relies heavily on explicit equations and physical principles, machine learning (ML) methods learn from large data sets containing experimental observations and possibly simulated wear data. By recognizing complicated patterns and relationships directly from these data sets, machine learning models bypass the need for explicit model parameterization and the manual formulation of wear physics. These methods are complex algorithms, often involving artificial neural networks (ANNs), adaptive neuro fuzzy inference systems (ANFISs), and support vector machine (SVM) analyses, as presented by [PERSON] [13], [PERSON] [14], [PERSON] et al. [15], and [PERSON] et al. [16], who developed data-driven models for predicting wear based on operational parameters. [PERSON] [13] used an ANN to predict the cuttability of rock with drag tools, while [PERSON] [14] used an ANN and ANFIS to predict the impact performance of hydraulic hammers. [PERSON] et al.