An Intelligent Milling Tool Wear Monitoring Methodology Based on Convolutional Neural Network with Derived Wavelet Frames Coefficient  
[PERSON]  
1 School of Aerospace Engineering, Xiamen University, Xiamen 361005, China; [EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (B.Y. )1  
[PERSON]  
1 School of Aerospace Engineering, Xiamen University, Xiamen 361005, China; [EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (B.Y. )1  
[PERSON]  
1 School of Aerospace Engineering, Xiamen University, Xiamen 361005, China; [EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (B.Y. )1  
[PERSON]  
2 New Jersey Institute of Technology, University Heights Newark, NJ 07102, USA; [EMAIL_ADDRESS]  
Footnote 1: email: {xincheng.ca,xiaobin,x  
monitoring system can be divided into three parts: Sensing and data acquisition, data processing and feature engineering, and pattern recognition and classification [8]. Sensing and data acquisition methods include direct acquisition and indirect acquisition. The contact detection is the most widely used direct approach, but the tool can only be measured after the end of one process. The indirect method that detecting the tool health condition by physical signals is more suitable for online measurement. The cutting force is the most mature indirect measurement method [9]. [PERSON] established a milling force model and then coupled the normalized tangential and radial forces in the model into a single parameter to monitor the wear of the end mill [10]. [PERSON] combines cutting force signals and vibration signals to identify tool wear and work piece deformation during processing of thin-walled parts [11]. [PERSON] adopts the cutting force to monitor tool wear and product surface quality during the CNC turning of D2 steel [12]. Nevertheless, the dynamometer is costly and does not fit large work pieces such as engine cases. Acoustic emission sensor [13], current/power sensor [14] and sound pressure sensor [15] also have their own advantages and disadvantages. In contrast, vibration acceleration sensing technology offers comprehensive advantages in terms of cost, flexibility, non-intrusiveness, information capacity and industrial reliability. [PERSON] applied torsional-axial vibrations to monitor flank wear during drilling, with an accuracy of 80% [16]. [PERSON] extracted the effective features for tool condition from the vibration signal under the interference of the cutting fluid [17]. The ever-increasing theoretical knowledge of sensing and signal processing can further enhance the reliability and signal-to-noise ratio of vibration sensors. [PERSON] proposed a closed loop calibration system to improve the calibration accuracy and efficiency of vibration sensors [18]. Signal processing and feature engineering aims to clean the raw data, segment and label the valid data, and further extract feature vectors to suit the needs of the decision subsystem. As a complex process system, no matter what kind of sensing method is used, we collect a mixture of various signals and noise. For the vibration signal, the unbalance of rotating parts, the inertial impact of reciprocating parts, and the insufficient smoothness of each shaft will produce forced vibration. [PERSON] used an adaptive variable window to locate the signature segments in the signal and successfully align them [19]. [PERSON] proposed a modified Karhunen-Loeve transformation algorithm for preprocessing acceleration signals [8]. For the original time domain signal, principal component analysis can be used to extract the effective features [20]. The regularization based on convex sparsity can be used to decompose noisy signals, while non-convex regularization can further promote the sparsity of reconstructed signals, while preserving the global convexity [21]. Studies have also shown that the method of extracting features from frequency domain signals is easier to implement and more stable [22]. The monitoring signals in the milling process are often similar to the modulated signals, and the information carried in different frequency bands is quite different.