# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 5 Tool Wear Monitoring Method Based on Deep Convolutional Neural Network
### Acquisition and Preprocessing of Tool Wear Monitoring Feature  
In the machining process, sensors are used to collect the information data related to the tool wear. The information is processed and input into the model for evaluating tool wear conditions.  
The acquisition and preprocessing of a single-sensor feature are described in Section 3. However, a single-sensor feature has certain limitations for system decision-making. Multiple sensors provide more redundant information, reducing the overall measurement uncertainty. Consequently, the accuracy and resolution of the model system are improved, and a better prediction performance is obtained. [PERSON] et al. [39] employed multiple sensors to collect seven time-domain features of the cutting force and acoustic emission. The cutting force features included the cutting forces \(\left(F_{x},F_{y},F_{z}\right)\) and accelerations \(\left(a_{x},a_{y},a_{z}\right)\) in the three directions. The features were subsampled into 5000 samples to form a tensor of (5000, 7), which was input into the CNNs for evaluating the flank wear condition. [PERSON] et al. [117] selected two features of the cutting force and cutting vibration as initial data, which was processed and input into the prediction model. Through training and testing the deep CNNs, the tool remaining life prediction model was established. [PERSON] et al. [104] indicated that the tool wear monitoring method using feature-level fusion significantly improved the accuracy of tool condition classification. High-precision monitoring was achieved through force, vibration and acoustic emission sensors, correlation feature selection methods, and majority voting machine integration. [PERSON] et al. [106] applied the proposed clustering method to analyze the wavelet characteristics of force and vibration features. The findings supported that the clustering method can roughly capture the process condition for fault diagnosis and tool wear monitoring. [PERSON] et al. [119] monitored the CNC tool wear condition by fusing the information of the force, vibration, and acoustic emission sensors. [PERSON] et al. [120] proposed a cutting force and vibration feature analysis method based on frequency and time-frequency for monitoring tool condition in the high-speed micro-milling. [PERSON] et al. [121] input the sound pressure and cutting force features to an expert system for monitoring tool wear. [PERSON] et al. [122] developed a multi-scale principal component analysis method based on force and vibration features for online tool wear monitoring during milling.  
Adding new features may offset the sensitivity loss of the original feature. Therefore, multi-sensing features can enhance the information richness on the potential wear level. However, more sensor features are not necessarily better. Adding sensors increases the system cost and maintenance difficulty. The more sensors, the greater the impact on the machining process, and the more complex the feature processing. More importantly, too much redundant information reduces decision-making accuracy. It is more effective to select a few feature parameters of critical sensors than to select more parameters [123; 124]. How to effectively set a few key feature parameters requires further in-depth study.