## 4 Discussion  
The examination of the LSTM and 1-D CNN models reveals significant insights into their performance, strengths, and limitations in classifying the given dataset. This discussion synthesizes the findings from both models, comparing their outcomes and elucidating the broader implications of these results. The LSTM model demonstrated commendable capability, particularly in identifying the 'Red' class, with a notable sensitivity of 92.00%. However, its performance varied across the 'Green' and 'Yellow' classes, indicating potential areas for improvement. The confusion matrix and performance metrics suggest that while the model can effectively recognize certain classes, their precision and sensitivity for others could be enhanced. The observed overfitting, as indicated by the divergence between training and validation accuracy, suggests that the model might benefit from regularization techniques or more varied training data to improve its generalization. Conversely, the 1-D CNN model showcased a robust ability to classify all three classes with high precision and sensitivity, underscored by an overall accuracy of 97.33%. Its performance is particularly noteworthy in the precise classification of the 'Green' and 'Red' classes, with both precision and sensitivity reaching or nearing 100%. The minimal discrepancy between training and validation metrics suggests that the 1-D CNN model has achieved a commendable balance between learning from the training data and generalizing to new, unseen data. Understanding which features or aspects of the sequence data are most influential in the classification decision is crucial for domain experts and enhances trust in the model's predictions. For the LSTM model, interpretability can be enhanced by examining the attention weights if an attention mechanism is integrated. This would highlight which parts of the sequence the model focuses on when making predictions. For the 1-D CNN model, interpretability can be improved by analyzing the learned filters in the convolutional layers. By visualizing these filters, we can identify which features of the signal, such as specific frequency components or temporal patterns, are most significant for distinguishing between different classes of tool wear. Additionally, techniques such as gradient-weighted class activation mapping (Grad-CAM) can be applied to visualize the regions of the input sequence that contribute most to the model's decisions. Figure 6: Training and validation accuracy and loss for the 1-D CNN. The findings of this study have significant implications and can be extended to other types of manufacturing processes or different types of tools. The methodology and models developed in this study can be adapted for monitoring tool wear in different manufacturing processes such as drilling, turning, or grinding. This would involve collecting appropriate sensor data for these processes and possibly retraining the models to accommodate the unique characteristics of the new datasets. Beyond milling tools, the models can be tested on other types of cutting tools, such as those used in woodworking or metal-cutting industries. This would require validating the models on data from these tools and ensuring that the feature extraction techniques are still relevant. Future studies could investigate the integration of more advanced regularization techniques, such as dropout or L2 regularization, to enhance the robustness of the LSTM model and reduce overfitting. Data augmentation methods could also be explored to increase the variability of the training data, further improving model generalization. Combining the strengths of LSTM and 1-D CNN models into a hybrid architecture could leverage the sequence processing capabilities of LSTM with the feature extraction prowess of CNNs. Such models could potentially offer improved performance over standalone models. Developing real-time monitoring systems based on the proposed models would be highly beneficial for industrial applications.