## 1 Introduction
However, these approaches had their limitations. Despite being more precise, they were still essentially reactive, meaning they detected wear only after it had occurred rather than predicting it before it manifested. Furthermore, they still required a high degree of supervision, albeit less than before, which could pose a problem in large-scale production scenarios where operating conditions vary considerably and resources for continuous monitoring may be limited. The need to overcome these limitations led to the development of predictive and more integrated methods for wear evaluation, and this is where artificial intelligence (AI) and Machine Learning techniques played a critical role. These technologies enabled the development of models capable of predicting wear based on large volumes of real-time data [9]. These models use Machine Learning algorithms to identify complex patterns that would not be apparent through traditional methods. By analyzing data such as cutting forces, vibrations, temperature, and other parameters, they can anticipate wear before it occurs, allowing operators to take preventive measures to avoid failures and optimize the performance of milling machines. As a result, AI models have been successfully applied to predict cutting tool wear, providing a more comprehensive and precise understanding of tool conditions to facilitate wear prevention in inserts. In recent years, various predictive models have been explored for cutting tool wear prediction, with approaches ranging from traditional Fuzzy Logic and Genetic Algorithms to more advanced Random Forest, Gradient Boosting, and Long Short-Term Memory networks. Each of these models offers distinct advantages and challenges, choosing models dependent on factors like data complexity, computation time, and the ability to predict wear patterns in real-time milling operations. Fuzzy Logic has been a popular approach in systems where inputs are uncertain or imprecise. It relies on fuzzy set theory to deal with partial truth values, making it useful for decision-making in environments with vague or ambiguous data. However, despite its strengths in modeling uncertainty, Fuzzy Logic struggles with the complexity of modern predictive tasks. The challenge of dealing with high-dimensional, continuous data from milling processes makes fuzzy systems less effective in this context, as they do not naturally capture the nonlinear, time-dependent relationships involved in tool degradation [10]. This makes them less suitable for dynamic and real-time wear prediction in complex manufacturing settings. Genetic Algorithms are another traditional approach used for optimization tasks, particularly in cases where the relationship between input variables is not easily defined. These models evolve solutions over generations through selection, crossover, and mutation. While they have proven effective for optimizing parameters like cutting speed and feed rate, they are computationally expensive and time-consuming, especially when applied to large datasets or real-time monitoring systems [11]. This limitation arises from the need for repeated evaluations of possible solutions, which can delay the prediction process, especially in environments where prompt wear detection is crucial. Given the limitations of Fuzzy Logic and Genetic Algorithms, other Machine Learning models like Random Forest, Gradient Boosting, and LSTM have gained prominence due to their ability to handle complex, high-dimensional data, provide faster predictions, and achieve higher accuracy in predictive maintenance tasks. Random Forest is an ensemble learning technique that constructs multiple decision trees and combines their outputs for robust predictions. It has become a widely used method in predictive maintenance because of its efficiency in handling large datasets with multiple variables [12].