## 1 Introduction
[PERSON] [17] proposed the use of least-squares support vector machines to determine the surface roughness based on cutting vibrations in turning operation. [PERSON] et al. [18] used fuzzy-net models for the prediction of the surface roughness, in which the feed rate, spindle speed, and tangential vibration were treated as model inputs. [PERSON] et al. [19] investigated the main cutting force and radial cutting force affecting the vibration of the flank wear. [PERSON] et al. [21] investigated the correlation between the cutting force and the surface roughness. [PERSON] [22] measured the cutting force and vibrations for predicting the surface roughness in turning operation. In general, dynamometer force sensors are generally large, expensive, and inconvenient to install. Vision image acquisition visualizes the surface roughness and directly predicts it; however, the system is unsuitable in a harsh environment, e.g., fogging via sputtered cooling oil and cooling water. Recently, in the development of an in-process and intelligent surface roughness prediction system, vibration signals of tool condition induced by a workpiece during milling operation are being used for surface roughness prediction. The interaction of all the machine parameters acting on the workpiece allow acquiring the vibration signal information generated from a machine tool body embedded with an accelerometer. The complex internal relations between surface roughness and machining parameters can be determined in the formulation of the surface roughness correlated with vibration signals using high performance prediction models. Based on observation, vibration signals are highly correlated with surface roughness in nature. Several researchers have used different vibration signals to predict the surface roughness [23; 24; 25]. [PERSON] et al. [24] constructed a multiple regression model to predict the in-process surface roughness of the workpiece in the turning operation based on vibration amplitudes, feed rate, depth of cut, and spindle speed; the result of the predicted accuracy was as high as 90%. [PERSON] [25] proposed mathematical models for predicting surface roughness based on machine tool vibrations and cutting parameters and used the models to observe the correlation between cutting vibrations and surface roughness. The above-described studies revealed that the prediction of surface roughness can be achieved by using vibration signals. The developed intelligent monitoring surface roughness system based on vibration signals could not only directly acquire the in-process surface roughness, but also ensure both the quality and quantity of the machined product. Thus, the aim of this research study is to verify the efficacy of a surface roughness prediction system based on the use of vibration signals. Recently, artificial intelligence (AI) techniques have been applied in vibration analysis. The AI technique is able to learn significant features from original historical data and can make a decision based on on-line data. Vibration analysis based on the AI technique usually involves four steps: data acquisition, feature extraction, model training, and model testing. The methods most commonly used for feature extraction methods are time-domain, frequency domain, and time-frequency domain methods. The extracted features are fed into the classifier, such as a support vector machine (SVM) [26; 27; 28] or a neural network (NN) [29; 30]. [PERSON] [26] proposed the use of the SVM technique for classifying tool wear states of surface images. [PERSON] et al. [27] used the SVM approach to identify tool flank wear. [PERSON] [28] proposed the use of the SVM algorithm to identify tool breakage abnormalities. [PERSON] [29] proposed a sequential diagnosis approach using the partially-linearized neural network to identify the fault rolling element bearing. [PERSON] et al. [30] proposed a neural network technique to monitor the health of a power turbine. [PERSON] [31] used a back propagation neural network model to predict the surface roughness in turning.