## 4 Discussion  
The objective of this investigation is to assess the tool wear of milling inserts through Machine Learning algorithms and data analysis. Numerous studies have explored the application of artificial intelligence techniques for monitoring wear in milling machines. According to a systematic review by [PERSON] et al. [6], there has been a notable increase in AI-related publications focused on machining operations since 2010, particularly concerning tool wear and the remaining service life of cutting tools. These studies highlight various Machine Learning algorithms, including artificial neural networks (ANNs), Fuzzy Logic algorithms (FLs), support vector machines (SVMs), decision trees and Random Forests (RFs), Bayesian networks (BNs), and convolutional neural networks (CNNs). By turn, in their research, [PERSON] et al. [24] proposed an innovative method for predicting tool wear using deep CNNs, known for their ability to learn hierarchical data representations through multiple convolutional layers. This CNN model architecture utilizes multi-scale convolutional kernels, which are crucial for capturing features of varying sizes in the input data. This capability is particularly relevant for predicting tool wear in machining processes, as it enables the model to recognize patterns across different scales, thus enhancing prediction accuracy. Additionally, Bayesian learning is introduced, incorporating principles of probability theory to manage uncertainty in predictions. By transforming the CNN model into a probabilistic framework, it becomes possible to estimate the uncertainty associated with wear predictions, which is vital for industrial applications. The integration of Bayesian learning allows the model to provide not only point predictions but also confidence estimates, making it especially valuable in processes like titanium alloy milling, where conditions can fluctuate and tool wear is unpredictable. While this model demonstrated a low mean square error and an R\({}^{2}\) score exceeding 0.9, our model achieved an R\({}^{2}\) value of 0.9857, surpassing the performance of the most advanced models, such as the Bayesian DMSCNN, which attained an average R\({}^{2}\) of 0.96. Notably, our model accomplished this with fewer cross-validation folds (5 vs. 10), fewer available data, and a broader range of variables, resulting in a very low mean MAE and MSE. This indicates that the model not only maintains competitive predictive accuracy but also demonstrates robust performance under challenging conditions. [PERSON] et al. [25] proposed an advanced approach for feature selection in predictive models by using singular value decomposition (SVD) combined with a voting scheme. In their study, the authors applied this technique to reduce the dimensionality of the dataset and then employed a voting scheme to select the most relevant features. This approach was complemented by three feature classification methods: Random Forest, Principal Component Analysis followed by Principal Component Regression (PCA + PCR), and Pearson Correlation Coefficient (PCC). The combination of SVD with PCA + PCR proved particularly effective, yielding the lowest mean square error [25]. This suggests that integrating advanced decomposition and feature selection techniques can significantly enhance the accuracy of predictive models by optimizing the relevance of the considered variables. Although our dataset was thoroughly cleaned and the features were handpicked to train the Gradient Boosting model, a careful examination of variables related to tool wear was conducted, adopting the findings of authors like [PERSON] et al. Their research highlighted the significance of specific features in predicting tool wear, which can be corroborated by their results through a correlation matrix. This matrix effectively validated their hypothesis, demonstrating that the selected features have meaningful relationships with tool wear, which further strengthened the predictive capability of our model.