Tool Health Monitoring Using Airborne Acoustic Emission and Convolutional Neural Networks: A Deep Learning Approach  
[PERSON]  
1 Department of Engineering Sciences, National University of Sciences and Technology, Islamabad 44000, Pakistan; [EMAIL_ADDRESS] (M.A. ); [EMAIL_ADDRESS] (K.K. ); [EMAIL_ADDRESS] (M.A.K. ); [EMAIL_ADDRESS] (T.A.H.R)  
2 Department of Mechanical Engineering, University of Management and Technology, Lahore 5470, Pakistan 23 Faculty of Mechanical Engineering, GIK Institute of Engineering Sciences and Technology, Topi 23640, Pakistan; [EMAIL_ADDRESS] (M.F.) 3 Faculty of Mechanical Engineering, University of Management and Technology, Lahore 54700, Pakistan 3 Faculty of Mechanical Engineering, GIK Institute of Engineering Sciences and Technology, Topi 23640, Pakistan; [EMAIL_ADDRESS] (M.F.) 4 Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia 44 Department of Engineering Sciences and Technology, Kap  
tool handling. Moreover, due to excessive vibrations or shock-loadings in the machining process, chipping occurs, which adversely affects the workpiece finish. These tool degradations should be monitored actively to avoid adverse consequences. In common practice, the tool health monitoring is done in the machine offline state, eventually increasing the machine downtime along with the increased risk of unpredicted sudden tool damages that could result in serious human injuries, work-piece, or machine damage. The CNC machining operation could significantly be increased using the robust online tool health monitoring system that would ensure real-time tool condition monitoring yielding toward minimal machine downtime or breakdown time. Whereas the utilization of CNC tools can be assessed through the worldwide CNC tool production industry profit figures for the year of 2016 to be around 67.6 billion Euros, the compound annual growth rate (CAGR) of this industry is predicted to be 9% [3]. [PERSON] et al. [4] proposed the hybrid technique that uses acoustic emission and the hidden Markov model (HMM) for tool wear prediction in titanium alloy (Ti-5553) during the milling process. Acoustic emission (AE) data of the milling process was acquired using an AE sensor that was installed at the back of the workpiece fixture. Machining parameters involving the axial depth of cut, radial depth of cut, spindle speed, and feed rate were set at 0.03 mm, 0.7 mm, 5082 rpm, and 4.268 m/min, respectively. After machining for a pre-determined time, tools were measured using an Alicona 3D optical microscope to classify various levels of wear, and once the state of the tools was confirmed, these tools were used to cut a material specimen while collecting monitoring signals from the process. Time-domain features like root mean square (RMS), mean, skewness, kurtosis, and peak signal values were extracted from AE recordings of the milling process and were presented to principal component analysis (PCA) for dimensionality reduction of the features. Four training states, new, used, worn-out, and damaged, were obtained by using multi-class support vector machines (SVM) on recorded data. The hidden Markov model was trained using a transition matrix and showed an accuracy of 98% for tool wear prediction. [PERSON] et al. [5] proposed a wireless sensor-based technique for tool condition monitoring in dry milling operations. The proposed technique uses a triaxial accelerometer to capture vibration signals that were further de-noised by wavelet analysis, followed by the extraction of time domain, frequency domain, and time-frequency domain features. Optimal feature selection was made based on Pearson's correlation coefficient (PCC), and these optimal features were used to train the Back-Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RFNN), and Neuro Fuzzy Network (NFN) to predict tool wear.