## 4 Discussion
In addition to the existing dataset limitations, future studies should prioritize the integration of environmental variables into the predictive modeling process. Factors such as temperature and humidity, which can significantly influence tool wear behavior, are not accounted for in the current dataset. To address this, we suggest the deployment of sensors capable of measuring these parameters during machining processes. The data collected from such sensors could serve as additional inputs for the predictive models, enabling them to capture a more nuanced understanding of the tool wear mechanisms under varying environmental conditions. Incorporating this layer of information would not only improve model precision but also enhance its applicability to real-world industrial settings where environmental fluctuations are prevalent. Another limitation of this study lies in the reliance on pre-collected data from public datasets, which, while convenient and robust, may introduce inherent biases or omit key variables relevant to tool wear prediction. Factors such as variations in machining setups, operational conditions, or even data collection methods can affect the generalizability of the findings. To address this, future research should prioritize the creation of tailored experimental datasets that capture a broader range of tool wear scenarios, incorporating diverse materials, machining environments, and operating parameters. Additionally, leveraging advanced data preprocessing techniques, such as bias detection and correction methods, can help mitigate the influence of such limitations, ensuring that predictive models are both accurate and suited for various industrial applications. Tool wear is a complex, dynamic process that is influenced by many variables, which are not only interconnected but also fluctuate over time, complicating the task of accurately modeling wear patterns. Current models, while valuable, are often limited in their ability to fully capture these dynamic interactions. As a result, future research should explore the application of advanced methodologies such as deep neural networks, which excel at modeling nonlinear and high-dimensional relationships. Furthermore, reinforcement learning offers the potential to continuously adapt to evolving wear conditions, providing a dynamic framework for more accurate predictions. These advanced approaches, coupled with transfer learning, could enable models to generalize more effectively across diverse machining environments, improving their applicability to real-world industrial settings. Future research could also consider the development of hybrid or ensemble models as well that combine the strengths of different Machine Learning approaches, such as Gradient Boosting and LSTM. These models could harness the complementary benefits of ensemble techniques and sequential learning to improve predictive accuracy and robustness. Additionally, integrating these predictive models into existing manufacturing execution systems would facilitate their practical implementation in industrial settings. Such integration could enable real-time tool wear monitoring and adaptive decision-making. Moreover, evaluating the model's performance on alternative test sets can yield valuable insights into its reliability across different scenarios. This practice is essential to ensure that the model is not overly fitted to a specific dataset, thus providing a more comprehensive understanding of its predictive performance. Additionally, implementing a grid search for hyperparameter optimization could facilitate the identification of optimal model parameters. This methodical exploration of parameter combinations allows for fine-tuning, which can lead to significant improvements in accuracy and predictive capability. By rigorously refining the model in this manner, it can be ensured that it remains both robust and adaptable to diverse datasets and operational contexts. Such efforts would ultimately bolster the model's applicability and effectiveness in real-world applications.