## 4 Conclusions  
Diverse approaches have been applied to initiating different measurable conditions on the cutting tool to generate signals during operation that can be analyzed to deduce the tool condition. Whereas some studies have artificially created a crack on the tool to capture signals (vibration, acoustics, etc.) capable of being analyzed to give feedback on the condition of the tool, some studies have measured the tool chip size to capture the signals produced by the tool condition and the product quality output. The former approach is limited by the possible tool damage or weakness caused by the method, whereas the latter is limited to the minimum size of the chip that can be measured and the dispersion or number of such chip points on the tool. Studies that have adopted image sensing as the input data source for analysis of the tool condition have shown this is also a daunting approach due to the obstructive nature of the cutting region, which allows contamination, hence reducing the viability of the captured image to accurately and precisely depict the condition of the tool. Besides the mentioned drawbacks, the variability caused by the different conditions surrounding image/vision sensing, such as illumination, and variation in pixel senses of different devices, have also limited the approach in the propagation of signals that can intelligently depict the tool and workpiece condition. The approach in this study focused on advanced signal processing and analysis, and feature extraction, selection, and classification, to determine the condition of the cutting tool during the machining operation without intermittent stoppage of machining. In this study, the cutting tool was first classified into four (4) classes using ISO 3685:1993 based on the flank wear (VB) of the cutting tool. The captured vibration signals were first decomposed using the EMD method to obtain the corresponding IMFs and residuals of the signals. To derive the feature vectors of the signals with instantaneous properties capable of classifying the tool classes, the HHT function was applied to the IMFs. It was observed that 12 features were obtained after applying the Hilbert transform to the IMFs. To reduce the computational burden and time, feature selection was performed with GA. After applying three (3) ML models to classify the tool conditions, with 5-fold and 10-fold cross-validation, the following observations were noted:  
1. Neural network feed-forward backprop with an SCG ML model was first adopted to classify the tool classes, with a fair error of 0.102. A better model was observed to provide better performance. 2. SVM and KNN were applied to feature classification with both 5-fold and 10-fold cross-validation, and the effectiveness of the models was evaluated by determining the error loss of both models. 3. The lowest error loss of 0.4752 was observed with the SVM model when 5-fold cross-validation was implemented, whereas, with the KNN model, the lowest error loss was observed to be 0.0166 when 5-fold cross-validation was implemented. In this case, feature selection using GA was implemented before ML classification. 4. The lowest error loss of 0.4881 was observed with the SVM model when 10-fold cross-validation was implemented, whereas,  
7. Of the two models, KNN performed better in classifying the tool classes during the machining operation when the decomposition method was applied to the vibration signals captured during the operation. 8. SVM models performed better when all the features extracted from vibration signals were considered, compared to when feature selection was implemented, whereas, for the KNN model, the performance was better when feature selection was implemented. 9. The methodology developed based on tool classification using advanced signal processing techniques can be used to classify product quality output based on work requirements in terms of roughness parameters.