## 2 Related Works  
[PERSON] [21] combined Multiple Linear Regression (MLR) and Random Forest Classifier (RFC) techniques to evaluate grinding capacity and detect the wear conditions of robotic belt grinding. Here, five distinct belts exhibiting varied tool wear conditions were used in the proposed simulation process, and 300 observations of grinding belt surface wear were used as training and testing parameters for the belt condition classifier. The fact that the data were fixed was considered one of the limitations. [PERSON] [22] combined time-frequency, frequency, and time domain feature extraction techniques to extract feature vectors from the vibration and force signals of the CNC machines. Next, a Gradient Boosting Decision Tree (GBDT) approach was applied for selecting the optimal feature vectors, and then classification was carried out using a hybrid method. Here, Prognostics and Health Management (PHM) challenge 2010 dataset was utilized to confirm the projected system. In this case, not every trait was automatically linked to tool wear. In addition, [PERSON] [23] segmented the cutting edges from the images of edge profile cutting heads, and then a local binary pattern descriptor was used to extract feature values from the segmented wear patch regions. Next, the Support Vector Machine (SVM) classifier was used to classify the cutting edges as serviceable or worn, and it was discovered that the overlapping between these patches began at the bottom of the main edge. [PERSON] and [PERSON] [24] conducted a descriptive statistical evaluation to extract feature vectors from drilling-induced vibration signals, after which, the K-star classification method was used for classifying the tool conditions: bad tool at high speed, bad tool at low speed, good tool at high speed, and good tool at low speed. However, because of its wear resistance properties, the tool proved challenging to cut. In reference [25], [PERSON] utilized a Long Short-Term Memory (LSTM) network for tool flank wear prediction that helped in obtaining better machined surface with low manufacturing cost. Using a validation set, the Root Mean Square Error (RMSE) for the LSTM networks with hidden layers was determined. The findings revealed that the most efficient LSTM contains two layers and eight hidden layers, as well as a lower RMSE. Nonetheless, the prediction behavior of the two-layer LSTM network surpassed the three-layer model. In reference [26], [PERSON] utilized raw signals as network inputs for monitoring the tool wear of a high-speed CNC machine. After acquiring the raw sensor signals, feature extraction was accomplished using a parallel residual network for extracting multiscale local feature vectors. Furthermore, a stacked bidirectional LSTM network was utilized to obtain the time series feature vectors related to the tool wear properties. The suggested LSTM had maximum convergence speed, but, at the same time, it exhibited high training loss. In reference [27], [PERSON] developed a Deep Convolutional Neural Network (DCNN) model for tool wear monitoring based on multidomain feature fusion. However, when used as a predictor for machine vibration, the DCNN algorithm would have been unable to detect tool wear in actual environments. [PERSON] [28] combined a CNN model and Derived Wavelet Frames (DWF) for tool wear state prediction utilizing machine spindle vibration signals. The reconstituted subsignals were then layered into a 2-D signal vector to replicate the design of a 2-D CNN, while simultaneously maintaining additional information. Unfortunately, this raised the network's complex nature, making it even harder to optimize and increase the likelihood of overfitting. [PERSON] [29] combined an SVM classifier and Genetic Algorithm (GA) to predict tool wear state. Initially, wavelet packet decomposition techniques, frequency domain, and time domain statistics were used to extract cutting force signal. Finally, the SVM classifier with Grey Wolf Optimizer (GWO) was developed to obtain the state recognition results.