## 6 Conclusions  
This research proposed an acoustic emission and deep neural network architecture-based technique to detect the tool condition and to predict the tool health in real-time with accuracy using just a single acoustic emission sensor. Industry-oriented CNC turning operations were performed in the machine workshop on three different types of commercially utilized materials to predict the tool health on three different degradation adversity levels: \"New,\" \"used,\" and \"worn-out\" tools. Elastic waves generated during machining of the workpiece, and the resulting airborne acoustic emissions were recorded using a standard microphone, at a typical sampling rate of 44,100 Hz on a laptop, without any special treatment or exclusive signal acquisition procedure. Frequency and temporal domain features were analyzed individually, concluding that there were no reliable and unique categorical characteristics on which a robust tool health predictor could be formed. These one-dimensional AE signals are then preprocessed to represent time, frequency, and energy attributes in two-dimensional visual illustrations called spectrograms, which contained rich information about the AE signal. Convolutional neural network (CNN) architecture \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**FS** & **FN** & & **Case 1** & **Case 2** & **Case 3** & **Case 4** & **Case 5** & **Case 6** & **Average** \\ \hline \multirow{4}{*}{\(5\times 5\)} & \multirow{4}{*}{4} & Accuracy & 1.000 & 0.980 & 0.998 & 0.988 & 0.988 & 0.998 & 0.992 \\ \cline{2-10}  & & Precision & 1.000 & 0.971 & 0.996 & 0.982 & 0.982 & 0.996 & 0.988 \\ \cline{2-10}  & & Recall & 1.000 & 0.970 & 0.996 & 0.981 & 0.981 & 0.996 & 0.987 \\ \cline{2-10}  & & F1 Score & 1.000 & 0.970 & 0.996 & 0.982 & 0.981 & 0.996 & 0.988 \\ \hline \multirow{4}{*}{\(3\times 3\)} & \multirow{4}{*}{4} & Accuracy & 0.744 & 0.583 & 0.970 & 0.570 & 0.973 & 0.985 & 0.804 \\ \cline{2-10}  & & Precision & 0.818 & 0.464 & 0.959 & 0.319 & 0.962 & 0.979 & 0.750 \\ \cline{2-10}  & & Recall & 0.616 & 0.374 & 0.956 & 0.356 & 0.959 & 0.978 & 0.707 \\ \cline{2-10}  & & F1 Score & 0.527 & 0.277 & 0.955 & 0.221 & 0.959 & 0.978 & 0.653 \\ \hline \multirow{4}{*}{\(10\times 10\)} & \multirow{4}{*}{4} & Accuracy & 0.556 & 0.852 & 0.556 & 0.575 & 0.840 & 0.556 & 0.656 \\ \cline{2-10}  & & Precision & 0.111 & 0.777 & 0.111 & 0.395 & 0.807 & 0.111 & 0.385 \\ \cline{2-10}  & & Recall & 0.333 & 0.778 & 0.333 & 0.363 & 0.759 & 0.333 & 0.483 \\ \cline{2-10}  & & F1 Score & 0.167 & 0.765 & 0.167 & 0.359 & 0.749 & 0.167 & 0.396 \\ \hline \multirow{4}{*}{\(5\times 5\)} & \multirow{4}{*}{2} & Accuracy & 0.990 & 0.652 & 0.657 & 0.728 & 0.852 & 0.960 & 0.807 \\ \cline{2-10}  & & Precision & 0.986 & 0.480 & 0.741 & 0.792 & 0.790 & 0.949 & 0.790 \\ \cline{2-10}  & & Recall & 0.986 & 0.478 & 0.485 & 0.593 & 0.778 & 0.941 & 0.710 \\ \cline{2-10}  & & F1 Score & 0.986 & 0.431 & 0.428 & 0.584 & 0.776 & 0.940 & 0.691 \\ \hline \multirow{4}{*}{\(5\times 5\)} & \multirow{4}{*}{6} & Accuracy & 1.000 & 0.988 & 0.837 & 0.686 & 0.556 & 0.960 & 0.838 \\ \cline{2-10}  & & Precision & 1.000 & 0.983 & 0.771 & 0.524 & 0.111 & 0.950 & 0.723 \\ \cline{1-1} \cline{2-10}  & & Recall & 1.000 & 0.983 & 0.756 & 0.530 & 0.333 & 0.941 & 0.757 \\ \cline{1-1} \cline{2-10}  & & F1 Score & 1.000 & 0.983 & 0.756 & 0.510 & 0.167 & 0.941 & 0.726 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance parameters for evaluation of CNN architecture for all cases of different number and sizes of filters including both trainings. was developed, aimed to provide highly accurate tri-categorical tool health prediction. The spectrograms images were fed to the CNN directly as input without undergoing any extensive feature selection method. Different sizes and quantities of convolutional filters were used to determine the best combination, and two trainings were done for all six cases. The second training was the retraining with the learned weights from the first training.