## 1 Introduction
Another study carried out by [PERSON], et al. [21] classified different levels of tool wear in the milling process, using vibrations, tool tip temperature, and cutting force, into medium and critical wear stages. This literature has illustrated that there is a link between the tool condition, the surface roughness of the workpiece, and the machine tool operating parameters during machining. However, extracting more features from the captured vibration signal may provide better analytical results when trained using machine learning (ML) algorithms. A larger number of extracted features provides more available data for the classification algorithm. Presently, most manufacturers use time as a conventional tool replacement strategy subject to the operator's experience [22]. This strategy often results in either an early replacement (under-utilizing the tool) or a late replacement, which often causes damage to both the workpiece and machine tool. Therefore, tool condition monitoring (TCM) has been considered a key enabling technology for manufacturing optimization [23]. The system estimates the tool condition by either deploying sensor-based models or analytical models [24]. [PERSON], [PERSON] [25] revealed that not all vibration feature extraction methods are appropriate for real-time monitoring of the surface finish, but the vibration signal with the wavelet packet transform (WPT) method can effectively be used for real-time surface finish monitoring, with high accuracy and reliability, and a low computational cost, in CNC machining. The study shows that the vibration signal is a better feature than the acoustic emission (AE) in monitoring the surface finish of the workpiece. In addition, [PERSON], [PERSON] [26] showed that tool wear and the machine vibration signal are related to each other at varying depths of cut and feed rates in different experiments. Although [PERSON], [PERSON] [27] proposed that the AE signal, when mounted on the tool holder rather than the workpiece, could enhance the reliability of the TCM system, in contrast, [PERSON] and [PERSON] [28] disproved the ability of AE sensors to distinguish between a new and worn-out tool when mounted on either the tool holder or the workpiece. It may therefore be of interest to note that the vibration signal is an important parameter in the TWCM system for depicting the state of the tool and the workpiece during the turning operation. Sensor-based tool and workpiece condition monitoring systems comprise highly sensitive sensors integrated with IoT controllers, which help with signal interpretation, analysis, and decision making. Like TCM, these systems can be divided into broad methods, which are direct and indirect [29]. The direct TCM method directly measures the changes in the geometry of the cutting tool during the cutting process, whereas the indirect TCM method measures the online operating parameters as a way of detecting the tool's deteriorating condition. Due to the nature of the cutting region during the process, however, the direct TCM method is challenging. Because direct TCM methods are grueling to implement online, most studies have adopted indirect TCM methods [30]. Similarly, TCM methods can be extended to monitor the surface quality of the workpiece using the same approach. This implies that the accuracy and precision of the measured conditions of the cutting tool, and of the workpiece/product quality, directly inform the accuracy and precision of the indirect online TCM system based on the acquired signals and the robustness of the processing techniques. The measured parameters are the data that are interpreted by the monitoring system to evaluate the current condition and the changing conditions; hence, more detailed attention needs to be paid to this system. Accelerometer sensors are devices that sense the displacement of a component or machine from its mean position [31]. The vibration effect during a cutting operation may result from several factors.