# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 5 Tool Wear Monitoring Method Based on Deep Convolutional Neural Network
### Deep Convolutional Neural Network
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Data Information** & **References** & **Main Sensors or Tools Used** & **Main Application Environment and Characteristics** \\ \hline acoustic emission & [64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77] & acoustic emission sensor & applied to milling, grinding, drilling, etc., real-time detection, sensitive, easy to be disturbed, high requirements for the materials of the test object \\ \hline temperature & [79; 80; 81; 82; 83; 84; 85; 86] & pyrometer, thermocouple, thermal imager & applied to milling, turning, etc., real-time detection, easy to use, low sensitivity, cannot be used in the scene with coolant \\ \hline current & [36; 87; 88; 89; 90; 91; 92; 93; 94; 95] & current sensor, hall effect sensor & applied to milling, drilling, etc., real-time detection, difficult to detect tiny features, greatly affected by noise \\ \hline power & [89; 90; 93; 97; 98; 99; 99] & differential power detector, power sensor, current divider & applied to drilling, turning, milling, etc., low cost, easy to use, real-time detection, rarely used alone \\ \hline workpiece surface roughness & [28; 29; 101] & roughness probe, roughness meter, laser sensor, infrared sensor & applied to milling, turning, etc., non-real-time detection, limited application \\ \hline multi-information fusion & [20; 26; 27; 103; 104; 105; 106; 107] & multiple sensors & various processing environments; widely used \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Here, the convolutional layer performs a convolution operation, similar to the filter operation in image processing. It is mainly responsible for feature extraction operations, and its output size is related to the input size, the convolution kernel size, the filling value size and the step size. The pooling layer is an operation to reduce the space in the length and height directions. The calculation formula of the output size is the same as that of the convolutional layer. However, the pooling layer is not filled in most cases. Thus, the output size of the pooling layer is only related to the input size, the convolution kernel size, and the step size. According to the principle of image local correlation, a small area of the input feature map is selected for feature pooling with the maximum or average value. Consequently, the computational amount is reduced, and it has a certain degree of noise immunity. Finally, the fully connected layer classifies the extracted features and outputs the final result (probability) according to different tasks. [PERSON] et al. [111] proposed an AlexNet network model consisting of five convolutional layers and three fully connected layers. The network innovatively used the rectified linear unit to solve the gradient divergence and applied the dropout method in the fully connected layer to avoid model over-fitting. [PERSON] et al. [112] developed the VGGNet, including network models with varying depths from eleven to nineteen layers. The VGG16 and VGG19 are commonly used, and the model structure adopts the five sets of convolutional layers, the three fully connected layers and the softmax output layer. The difference is more and more cascaded convolutional layers involved in the convolutional layers. The model verified that increasing the depth helps to improve the classification accuracy. [PERSON] et al. [113] proposed the GoogLeNet with an inception structure, using a large number of 1 \(\times\) 1 convolution kernels. It significantly reduced the parameter number, improved the training speed and promotion ability of the model, and realized the approximation of the dense component to the optimal local coefficient structure. Moreover, two auxiliary classifiers were added to the model to conduct the gradient forward, effectively reducing the phenomenon of gradient disappearance. [PERSON] et al. [114] proposed the ResNet, which directly transferred input information to output.