The NFN showed the best performance for the prediction of tool wear with a mean squared error (MSE) of \(3.25\times 10^{-4}\) and mean absolute percentage error (MAPE) of 0.0224. The experimental setup involved a mini-CNC milling machine, tempered steel (HRC52) workpiece, micro-grain carbide two-flute end milling cutter coated with multilayer coatings of titanium aluminum nitride (TiAlN), a wireless triaxial accelerometer mounted on the workpiece to measure the vibration, and a wireless base station to process the vibration signal and transmit these signals to a computer through the local area network (LAN). [PERSON] et al. [6] proposed the use of acoustic emission for tool condition monitoring in the turning process. The proposed technique makes use of acoustic emission and wavelet analysis to monitor tool wear during turning operations. Machining tests were carried out using an NC turning center, mild steel workpiece, and tungsten carbide finishing tool. Experiments were performed using sharp and worn-out tools with different machining conditions. A piezoelectric AE sensor was mounted on the tool holder with a light coating of petroleum jelly to ensure good acoustic emission coupling. A high-pass filter was applied to the acquired AE signals to wipe out the low-frequency noise components. Time-domain and frequency-domain plots for the acquired AE signals of the sharp tool were distinguishable from the plots of the worn-out tool. The sixth-scale wavelet resolution coefficient norm was extracted for the sharp and worn-out tool separately using wavelet analysis. The coefficient norm of the signals from the sharp tool was much more stable than that of the worn-out tool. [PERSON] et al. [7] proposed a sensor fusion and artificial neural network (ANN)-based approach for tool condition monitoring (TCM) in metal cutting operations. The proposed technique uses the Kistler triaxial accelerometer and Kistler charge amplifier to record the static cutting force, dynamic cutting force, and vibration signals during turning operation on new and worn-out tools. The obtained data were used to investigate the classification capability of simple Multi-Layer Perceptron (MLP) neural network architectures to the detection of tool wear. Obtained results showed that a classification accuracy of well over 90% was attainable. A major advantage of using acoustic emission (AE) as a tool condition monitoring technique is that the frequency of AE signals is much more dominant than those of machine vibrations and environmental noise. Back-Propagation Neural Networks (BPNNs) have been extensively used to model relations between tool states and signal features extracted from the acoustic emission sensor, vibration sensor, and dynamometer [8; 9; 10]. However, industrial tool condition monitoring cannot be performed via BPNN, due to its slow computing speed in online modelling applications. Due to their improved on-line computing speed, [PERSON] et al. [8] proposed the use of a Localized Fuzzy Neural Network (LFNN) for tool condition monitoring in turning operations. Experiments were conducted on a conventional lathe to acquire force signals with a dynamometer and AE signals with an AE transducer. Time domain, frequency domain, and frequency-time domain features were extracted from both force and AE signals. Twelve relevant features were selected using the synthesis coefficient approach as a feature selection technique. Adaptive learning was used to train an LFNN to model the relationship between extracted features and tool wear amount with high precision and good on-line computing speed. Several time-domain and frequency-domain features of acoustic emission released during machining have been used with conventional neural networks for tool health monitoring applications. The proposed approach uses time and frequency domain-fused spectrographs with deep learning-based convolutional neural networks for tool health monitoring applications.