## 3 Results and Discussions
[PERSON], [PERSON] [63] applied SVM and logistic regression (LR) to classify the vibration signals at varying bearing speeds, and the result showed that the LR model yielded a poorer prediction. Similarly, whereas [PERSON], [PERSON] [42] classified vibration signals based on variational mode decomposition (VMD) and energy entropy using the SVM technique, for  
Figure 5: Training State of Neural Network with SCG algorithm. fault diagnosis in rotating bearings, [PERSON], [PERSON] [64] applied a linear support vector machine (LSVM) for the classification of data between two classes of signals captured from the vibration of an induction motor machine by finding the best hyperplane. However, [PERSON], [PERSON] [65] classified EMD features and FFT features extracted from vibration signals to diagnose bearing faults without any statistical information using KNN classifiers, with the method yielding a reduction percentage of 96.64%. The result of this classification algorithm showed a good performance, even though the study applied FFT to the decomposed signal, in contrast to this study, which applied HHT to the decomposed signal. Similarly, [66] applied both KNN and SVM to features extracted from vibration signals processed through FFT and principal component analysis (PCA), and showed that vibration signals are sufficiently rich in information about the machine to enable precision machining and 100% state classification accuracy to be achieved. Hence, SVM and KNN  
Figure 6: Error Histogram of SCG Neural Network Model. Figure 7: All Confusion Matrix for SCG Neural Network Model. were applied to the extracted features for the classification of the conditions in other phases to determine which classification model performs optimally for classifying tool classes. Bias and misclassification error are mostly a challenge when applying ML algorithms and techniques to classification problems. Therefore, to avoid these, k-fold cross-validation techniques are applied. This study applied 5-fold and 10-fold cross-validation, and the models were compared to determine the one with the best performance in terms of the error loss in classification. For the 5-fold cross-validation technique, the models were developed and tested by determining the error loss for both SVM and KNN to determine the model with better performance. Figure 8 illustrates the error loss of each model of the SVM and KNN methods, with the blue color representing SVM models and red representing KNN models. The result showed that the KNN algorithm performs better than SVM in classifying the cutting tool classes during the machining operation. To determine the overall error loss for each of the models, Equation (13) was applied:  
\[E=\frac{1}{k}\sum_{i=1}^{k}E_{i} \tag{13}\]  
where \(k\) represents the number of folds being considered and \(E\) is the error loss. Therefore, the overall error loss for the five SVM models was 0.5031, while for the KNN model it was 0.0318. This indicates that KNN models performed better than the SVM models for tool condition classification during the machining process. For the 10-fold cross-validation technique, both models were also developed to determine their performance. Figure 9 shows that the KNN models all performed better than the SVM models. Each SVM model from 1 to 10 had a higher error loss function compared to the KNN models. Figure 8: Error Loss for SVM vs. KNN classification with 5-fold Cross-validation. The overall error loss for the SVM models with 10-fold classification was 0.5009, whereas the error loss for KNN models was 0.343. The general performance of the two (2) models shows that the error loss of SVM slightly improved when the 10-fold cross-validation technique was used, compared to the 5-fold cross-validation technique. Furthermore, the performance of KNN models was better with the 5-fold cross-validation technique than with the 10-fold cross-validation technique. In Figure 9, the KNN8 model is the best, with the least error loss, while the SVM5 model is best for the 10-fold cross-validation.