## 1 Introduction
A cutting tool that is broken or worn-out during a cutting operation can cause a resultant vibration effect on the machine tool. Similarly, a faulty jaw can induce spindle vibration, which affects both the tool condition and the workpiece [32]. [PERSON] and [PERSON] [33] detected varying conditions, such as a normal spindle, an unbalanced rotor, a gearbox crack, and a bearing crack, on a CNC machine tool by analyzing vibration signals captured by accelerometer sensors installed on the spindle surface near the bearing. This captured the vibration signal resulting from malfunctioning of the machine tool component, which in turn affected the surface roughness of the workpiece and tool wear. In contrast, [PERSON], [PERSON] [34] deployed a magnetic-type accelerometer, attached to the spindle bearing housing, to capture the vibration signals from the machine tool during the machining of AISI 1040 carbon steel. Furthermore, [PERSON], [PERSON] [35] noted that an abrupt increase in the spindle rotation frequency to the natural frequency of the spindle structure increased spindle vibration, which degraded both the surface of the workpiece and the spindle performance. Therefore, capturing the vibration signals using an accelerometer is of great interest in monitoring the surface roughness of the workpiece and the tool condition. Signals captured from the monitoring devices installed on the machine tool can be classified as steady-state signals and dynamic/transient signals. The former are captured when the condition of the machine is stable during operation, whereas the latter are captured during the unstable operating condition of the machine tool. These two distinctive categories of signals captured from the machine tool have been analyzed using different techniques and methods to extract intelligent information that is useful in depicting the state of the process. Different signal processing methods can be adopted for the TWCM system. Whereas time domain analysis techniques are used to evaluate physical signals and mathematical functions with reference to time [36; 37; 38; 39], frequency domain techniques are used to analyze signals or mathematical functions with reference to frequency [40; 41; 42]. Signals can be converted from either the time domain to the frequency domain or vice versa with an operator called a transform. Early research on the TCM system adopted the Fourier transform, which converts a time function into an integral of sine waves of various frequencies; however, this is deficient for the analysis of non-periodic and non-stationary signals, hence, other types of transforms have been developed [43]. Wavelet packet transforms (WPIs) have shown better results as a computational method for time-frequency signal conversion and, as a result, have been widely used in many tool condition monitoring studies [44; 45; 46; 47]. A comparative study to predict bearing degradation [48] using a discrete wavelet transform (DWT), tabular Generative Adversarial Networks (TGANs), and ML models also showed that the DWT is an efficient signal processing tool for decomposing signals that are non-stationary signals. Other transforms exist for signal decomposition and computing time-frequency conversion, such as the Hilbert-Huang transform [49]. In addition, artificial intelligence techniques are another type of signal-processing method used for TCM systems [50]. This technique was adopted by [PERSON] and [PERSON] [51] for fault prognosis and condition monitoring using the previous data to predict the remaining useful life (RUL) of the component using various regression/degradation models. Due to the robustness and strength of this technique in the analysis of large volumes of data, and the development of intelligent models for predicting the condition of the workpiece and tool, the usage of this method has increased recently in TCM systems [52; 53]. Several AI algorithms have been deployed in either predicting or classifying tool or workpiece conditions at a machining station.