## 4 Discussion
### Improvements Achieved  
During this study, the following key improvements were realized:  
* Feature selection for SVM: Implementing sequential feature selection (SFS) significantly enhanced the performance of the SVM model by reducing the feature space and improving the model's ability to generalize from the training data.
* Optimizing boosting ensemble decision trees: Utilizing Bayesian optimization for hyperparameter tuning led to a more efficient and accurate boosting ensemble decision trees model. This improvement minimized model overfitting and maximized its predictive accuracy.  
* Enhanced data preprocessing: Improved data preprocessing techniques, including standardization and normalization, ensured that the models received high-quality inputs, which in turn improved their performance.
* Comprehensive evaluation framework: The implementation of k-fold cross-validation provided a robust framework for evaluating the models, ensuring that the performance metrics were reliable and representative of the models' capabilities.
* Integration of domain knowledge: Incorporating domain-specific knowledge in the feature engineering process helped in selecting the most relevant features, improving the model's ability to discern between different tool states.
* Real-time monitoring capabilities: Developing a framework for real-time data acquisition and processing enabled immediate feedback on tool conditions, which is crucial for industrial applications.
* Feature engineering: Developing novel feature engineering techniques that transform raw sensor data into more meaningful representations, enhancing the models' ability to learn from the data.
* Cross-validation techniques: Applying advanced cross-validation techniques, including a stratified k-fold approach, to ensure that each fold was representative of the overall dataset, thereby improving the robustness of the evaluation.
* Automated hyperparameter tuning: Implementing automated hyperparameter tuning methods, such as grid search and random search, ensured that the models were optimized for performance without manual intervention.
* Balanced dataset: Ensuring that the dataset was balanced across the three classes (green, yellow, red) prevented biased training and testing, which contributed to more accurate and generalizable models.  
These improvements have collectively contributed to the advancement of machine learning techniques for tool condition monitoring in milling processes, demonstrating the potential for more effective and reliable predictive maintenance in industrial settings.  
The key improvements achieved in this study underline the importance of meticulous feature selection, optimized hyperparameter tuning, and robust data preprocessing. These enhancements not only improved the individual performance of the SVM and boosting ensemble decision tree models but also set a foundation for future research that could explore the integration of these methodologies with more complex models like LSTM. The balanced dataset and comprehensive evaluation framework further ensured the reliability and applicability of the findings, paving the way for the development of more sophisticated, domain-specific predictive maintenance solutions.