# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 5 Tool Wear Monitoring Method Based on Deep Convolutional Neural Network
### Network Model Structure for Tool Wear Monitoring  
According to the actual requirements of tool wear monitoring, the classic network model needs to be modified. [PERSON] et al. [32] borrowed from the residual network design ideas for arrhythmia training proposed by [PERSON]'s team. They established the CNNs to predict tool wear conditions based on information related to cutting force, e.g., spindle load and spindle torque. The network structure is shown in Figure 7. The batch normalization (BN) layer is added to each layer in the model. The rectified linear units (ReLU) behind the BN layer represent the nonlinear transformation of the feature matrix, using the modified linear activation function \(f(x)=\max(0,x)\) to operate on all elements in the feature matrix individually. The dimension of the feature matrix remains unchanged after the ReLU conversion. The function increases the network sparsity and improves the representativeness and generalization of the extracted features. [PERSON] et al. [36] regarded the currents of the three axes as the three input channels of the network. The first convolution layer uses a large convolution kernel of size 21. The size of the subsequent convolution kernel is reduced layer by layer. The step size of all convolutional layers is two, halving the feature map size. The pooling layer is connected to the back of each convolutional layer. The maximum value of the convolution kernel size and step size is two, halving the feature map size again. The channel number in the first convolutional layer is 32. The channel number in each subsequent network layer is increased by two times or remains unchanged. The output size of the last pooling layer is \(3\times 128\), flattened into a 384-dimensional vector. Finally, two fully connected layers are connected for classification. The BN layers are added after each convolution operation and before the activation function. To avoid network over-fitting, random dropout technology is applied to the flattening layer after the last pooling layer. During the training process, each neuron output in the hidden layer is set to zero with a probability of \(p=0.5\). As a result, the interdependence between neurons is reduced, thereby decreasing the structural risk. The network structure is shown in Figure 8.  
Figure 7: The CNN model structure proposed by [PERSON] et al. (drawn from the literature [32]). [PERSON] et al. [62] improved the training model for ImageNet images proposed by the [PERSON] team. They used the original vibration feature in the machining process, converted into an energy spectrum by a wavelet packet as the model input. The optimal parameters trained by [PERSON] were used as the initial model parameters. The output of the original model was modified to three types, which were accurately applied to the monitoring and prediction of tool wear. The modified CNN structure is shown in Figure 9.  
[PERSON] et al. [116] modified the AlexNet model trained by [PERSON], taking the grey-scale wear image with a pixel size of \(256\times 256\) as an input to divide the tool wear into four types. Consequently, a CNN structure was developed, which could intelligently identify the tool wear type. The convolution kernel size of the C1 convolution layer in the AlexNet model was modified to \(14\times 14\times 96\). The step size was adjusted to five. The edge expansion parameter was modified to fourteen. Additionally, the final fully connected layer (i.e., output layer F8) was modified to four output types. The modified CNN structure of tool wear type recognition is shown in Figure 10.  
Figure 8: The network structure of tool wear monitoring based on current features proposed by [PERSON] et al. (drawn from the literature [36]). Figure 9: The network structure of tool wear monitoring based on vibration features proposed by [PERSON] et al. (drawn from the literature [62]). [PERSON] et al.