## 1 Introduction  
A tool condition monitoring system has drawn a great deal of attention in the industry lately due to the rapid development of intelligent manufacturing systems. At the same time, the demand for micromechanical machining in the industry has also been rapidly increasing due to the increasing development of products with small features. The low signal strength and limited space for sensor installation when dealing with small parts make it more challenging to monitor the micro tool condition than in cases with conventional tools sizes. Although the tool condition monitoring system for the conventional size of tools has been studied for decades and a number of review papers have been reported [1; 2; 3; 4; 5; 6; 7; 8; 9], there are still several challenges regarding implementing the laboratory-developed system to the production line, including the noise, system uncertainty, and the complex cutting paths inherent in milling processes. One of the key solutions is to obtain reliable signal features during cutting. Different types of sensors have been investigated in previous studies for tool wear monitoring, including the current sensor [10; 11], dynamometer [12; 13; 14; 15; 16; 17], accelerometer [18; 19; 20; 21], acoustic emission (AE) sensor [22; 23; 24; 25; 26], microphone [27; 28; 29; 30], and vision sensor [31; 32]. Multi-sensor systems were also studied to improve system reliability [33; 34; 35]. Recently, classifier design has become another focus in the development of a tool wear monitoring system. Several types of signal processing algorithms were investigated, including the linear classifier design [27; 36], a neural network [18; 19; 20; 21; 33; 37; 38], fuzzy logic [39; 40], a decision tree [25], the hidden Markov model (HMM) [14; 41], and AutoEncoder [42]. However, straight line cutting was always implemented in these laboratory works to develop and evaluate the tool wear monitoring system in milling. For the investigations of vibration and AE signal for tool wear monitoring, [PERSON] and [PERSON] [18] applied the spindle vibration signal and a back-propagation neural network to monitor the micro tool wear in a straight line micro milling process on SK2 steel and demonstrated that the proposed methods hold the potential to be implemented in the production line. [PERSON] et al. [19] proposed a tool wear monitoring method using vibration signals collected from accelerometers on the workpiece and developed classifiers based on short-time Fourier transform (STFT) and a deep convolutional neural network (DCNN) in straight line milling operations. The experimental results prove that the proposed method is more accurate and relatively more reliable than other compared methods, such as SVR, PSO-SVR, and PSO-LSSVR. [PERSON] et al. [34] monitored the wear of cutting tools in the micro milling process using an SVM (support vector machine) artificial intelligence model, along with the vibration and sound signals. The straight line microchannel was used to collect signals in the experiments. A classification accuracy of up to 97.54% was obtained in the evaluation. [PERSON] and [PERSON] [23] presented a study on tool condition monitoring using a workpiece AE signal in the micro-end milling of different materials, including aluminum, copper, and steel alloys. A strong relationship between the tool wear (flank wear) and acoustic emission (AERMS) signals could be observed in this study. [PERSON] et al. [24] used the AE signal on the workpiece to monitor four states of tool wear conditions in the channel milling of titanium alloy Ti-6 Al-4V under MQL conditions. Support vector machine (SVM) integrated with linear discriminant analysis was adopted as the classifier. An overall 98.9% classification rate was obtained in this study. In 2021, [PERSON] et al. [25] reported the use of the AE signal on a workpiece to monitor flank wear in the straight line milling of aluminum-ceramic composite containing 10% SiC. A decision tree was adopted in this study to identify the tool condition.