## 5 Conclusions  
This research provides a comprehensive analysis of tool wear in milling machines, focusing on the precise prediction of remaining tool life through the implementation of artificial intelligence algorithms. Throughout the development, the purpose of milling machines and their cutting tools, particularly milling inserts, is thoroughly examined, highlighting the advantages they offer over solid tools. The phenomenon of tool wear is explained in detail, emphasizing its direct impact on product quality and process efficiency, culminating in a direct application of Machine Learning models to accurately predict wear in insert blades. One of the key achievements of this work is the improvement in the accuracy of predictive models compared to what has been previously reported in the literature. The use of a simple Gradient Boosting model enables a more accurate prediction of wear compared to other models like neural networks and SVMs and is relatively close to that of more deep learning models that require extensive tuning and large datasets. Furthermore, it overcomes limitations observed in prior studies, where wear predictions relied on overly complex methods and a greater number of variables, obtaining an even lower R\({}^{2}\) score than our model. Additionally, a proper relationship between variables is established, explaining the correlation between the different variables involved. Specifically, this work demonstrates that AI models, when properly tuned (cross-validation, train-test splitting, and regularization) and applied, can effectively capture the complex relationships among the involved variables, thereby enhancing the accuracy and reliability of their predictions. The proposed methodology not only addresses the challenges of tool wear prediction but also provides valuable insights for future research in the field of machining. By employing a data-driven approach that integrates advanced AI techniques, this study lays the groundwork for developing more robust predictive models that can adapt to various machining conditions and contribute to solving potential limitations that could appear in these processes. Moreover, the incorporation of uncertainty awareness in predictions paves the way for more informed decision-making processes within manufacturing environments. As industries increasingly shift towards smart manufacturing, the findings of this research can significantly enhance operational efficiency and product quality, ultimately leading to improved customer satisfaction as products will be affected by cost reduction. The methodology employed in applying AI could be transferable to other manufacturing processes by adapting the algorithms. This study underscores the importance of harnessing AI algorithms in machining processes to achieve reliable tool wear predictions. Future work could explore the applicability of these models in different manufacturing contexts and investigate the integration of real-time monitoring systems to further enhance predictive accuracy and reliability. Finally, as previously noted, wear is a complex phenomenon influenced by numerous factors, including forces, vibrations, temperature, and material properties. While this model offers a reliable approximation, it could be significantly enhanced by integrating more relevant parameters related to the process. Using sensors and other techniques to collect real-time data thereby will improve input accuracy and ultimately enhance output predictions with artificial intelligence techniques. Conceptualization, S.V. and M.P.L. ; methodology, S.V., M.P.L. and J.L. ; software, J.L. ; validation, S.V., M.P.L., J.R. and J.L. ; formal analysis, S.V., M.P.L. and J.L. investigation, S.V., M.P.L., J.R. and J.L. ; data curation, J.L. ; writing--original draft preparation, S.V., M.P.L. and J.L. ; writing--review and editing, S.V., M.P.L. and J.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.