# Intelligent Air Cutting Monitoring System for Milling Process
## 1 Introduction
The monitoring of the machining process is required to investigate the air cutting in the machining process. Several works related to machining process monitoring have been reported. [PERSON] [8] reviewed several device-monitoring methods for observing tool wear. This showed that tool wear could be monitored by using acoustic emission (AE), cutting force, and vibration signals. [PERSON] et al. [9] described a mathematical model of fast Fourier transformation, and used it for tool wear monitoring based on acoustic emission signal. [PERSON] et al. [10] reviewed different methods of tool condition monitoring systems, including the use of acoustic emission, vibration, cutting force, and thermal. [PERSON] et al. [11] used acoustic emission and cutting force signal for tool condition monitoring in micro-milling. It showed that AE signal was strong and had very short reaction time to the tool-workpiece contact, which makes it a very good method for detecting the contact and monitoring the integrity of the cutting process. [PERSON] et al. [12] collected the power data during dynamic machining processes to diagnose and detect anomalies. [PERSON] et al. [13] used indirect data from CNC machines, such as spindle power, to automatically monitor tool breakage. The spindle power data were analyzed according to the relationship between the spindle power and NC block number to effectively and accurately detect tool breakage. [PERSON] et al. [14] used vibration signal to monitor the chatter signal, then combined it with response surface methodology for predicting the tool chatter. [PERSON] et al. [15] predicted the tool life by using machine spindle power and a neural network technique. Experiments with different spindle speeds and spindle power were recorded, then the neural network curve fitting with different training functions was applied to the root mean square power values to predict the tool life. [PERSON] et al. [16] proposed an Artificial Neural Network method with input parameters of spindle motor current, drill diameter, spindle speed, and feed rate to predict drill flank wear. The experiment results showed that the accuracy prediction using a neural network was better than that using a regression model. [PERSON] et al. [17] proposed a method that used electrical power for online fault monitoring in machine tools. The major advantage was the measurement did not interrupt or delay the machining processes. [PERSON] et al. [18]approached a hybrid optimization method to find optimal cutting parameters, which resulted in shorter machining time and lower power consumption for the milling process. The experimental results showed that the proposed method can effectively improve the efficiency and reduce machining time and electrical power consumption. Similarly, [PERSON] et al. [19] considered electrical energy for machining efficiency. [PERSON] [20] used the cutting process and the corresponding time series model to measure the vibration signal. It can predict real-time processing status through theoretical analysis and experiment methods. [PERSON] et al. [21] proposed intelligent online monitoring and a General Algorithm optimization system. The data were collected via sensor and Labview, then the experiment was carried out to verify the effectiveness of this method. In the previous study, the researchers investigated the air cutting path using a CAD model and the micro and macro process to generate NC machining; however, the air cutting path may occur, and also the air cutting occurrence during machining processes cannot be detected in the beginning. In this study, an intelligent air cutting monitoring system is proposed to improve the machining efficiency. The spindle current load, cutting vibration, and NC processing during the machining processes are measured and monitored in real time. Furthermore, an algorithm adopting these change states to determine effective cutting or air cutting was developed. An intelligent air cutting monitoring system with human-machine interface was built using C# language program.