## 1 Introduction
### Literature Review
#### 1.1.2 Deep Learning-Based Methods  
Machine learning-based methods are often weak in dealing with complex nonlinearities, and the model performance depends on manual feature extraction, which is implemented independently from model building. These characteristics limit the prediction accuracy and generalization of machine learning-based methods under complex conditions [13]. Compared with machine learning-based methods, deep learning methods have great feature engineering capabilities, which make them robust to data variation and capable of modeling complex nonlinear relationships [14]  
[PERSON] et al. proposed a hybrid information model that combines process information and temporal features based on a long short-term memory network (LSTM), where the LSTM was used to extract deep features for tool wear prediction [15]. [PERSON] et al. used LSTM for tool flank wear prediction within spindle motor current signals [16]. [PERSON] and [PERSON] a two-stage approach integrating tool wear-state classification and prediction based on bidirectional LSTM using current signals of spindle motor [17]. [PERSON] et al. proposed a heterogeneous gated recurrent unit (GRU) model integrating bidirectional GRU and unidirectional GRU models, and ten types of time, frequency, and time-frequency domain features were extracted as the input features [18]. [PERSON] et al. proposed a GRU-based tool wear prediction method, where seven types of time-domain features, two types of frequency-domain features, and sixteen wavelet energy values extracted via wavelet packet decomposition were constructed as the input features [19]. [PERSON] et al. used a convolutional neural network (CNN) for tool wear prediction with a hybrid feature extraction method integrating wavelet time-frequency transformation and spectral subtraction algorithms [20]. [PERSON] et al. proposed a CNN-based tool condition monitoring model using the spectrogram features of acoustic emission signals [21]. [PERSON] et al. proposed a deep CNN-based model with multi-domain feature extraction for tool wear prediction [22]. [PERSON] et al. proposed a CNN with a residual block based on dilated convolution for feature extraction from vibration signals, followed by fully connected neural networks for tool wear prediction [23]. By combining the CNN to extract spatial features between signals and the LSTM to extract temporal features in signals, several composite models have been proposed. [PERSON] et al. proposed a parallel residual and stacked bidirectional long short-term memory (PrespSBiLSTM) network for tool wear monitoring, in which the parallel residual network was used to extract local features, and a stacked bidirectional LSTM was used to extract time features [24]. [PERSON] et al. proposed a holistic-local LSTM to predict tool wear with a CNN for feature extraction, and the experiment showed that this ensemble model performed better than the CNN, LSTM, and bidirectional LSTM [25]. [PERSON] et al. proposed a parallel structure integrating a CNN and bidirectional LSTM, and before that, the local features were assigned with different weights based on the attention mechanism [26]. [PERSON] et al. proposed a hybrid attention-based parallel model integrating a residual neural network with a convolutional block attention module (CBAM) and bidirectional LSTM with a general attention unit [27]. Recently, graph neural networks (GNNs) are becoming popular to deal with graph-structured data and achieve significant progress in multivariate time-series prediction [28]. [PERSON] et al. proposed a graph convolutional network (GCN) with a spatial-temporal-event cross-attention mechanism to predict precise traffic flow [29]. [PERSON] et al. proposed a dynamic GNN to predict vehicle trajectory, and a vehicle interaction mechanism for dynamic movement was introduced to capture temporal and spatial dependencies among vehicles [30]. [PERSON] et al.