## 1 Introduction
[PERSON] [17] employed the least-squares SVM method along with the cutting conditions (feed rate, cutting speed, depth of cut) and the vibration signal feature to estimate an in-process surface roughness in turning processes. Considering the sensitivity of vibration signals to the background noise, signal analysis using the convection signal condition method is ineffective. Moreover, different features of vibration signals are extracted manually, resulting in the problem of low generality. An effective feature extractor is required to automatically extract useful signal features. In addition, the conventional predictive models belong to shallow layer structures, making it difficult to learn the complex nonlinear relationships involved in vibration signal prediction problems. Researchers have investigated the use of a convoluted features extractor and deep architecture to make significant progress in various application fields. The multi hidden network architecture proposed by the pioneer [PERSON] [32] possesses the following outstanding advantages. Deep layers in the convolution operator could extract important features from raw data and preserve the characteristics of parallel computing and self-learning features in shallow networks that could explore more natural feature information from complex data. Explosive applications are expanded in the signal analysis and machine fault detection problems. [PERSON] et al. [33] used one-dimensional convolutional neural network (1-D CNN) and long short term memory (LSTM) for vibration signal analysis to perform bearing fault diagnosis. [PERSON] et al. [34] used a 1-D CNN model with raw data to perform real-time motor fault detection. [PERSON] et al. [35] designed a deep LSTM model to predict the actual tool wear based on raw sensory data. Although deep learning techniques have been widely applied in the machinery industry, little effort has been applied to predicting the surface roughness of a monitoring system during the milling process using deep learning architecture. The two types of deep learning models--convolutional neural network (CNN) [34; 36] and LSTM [33]--have had their outstanding performances tested and verified in detection and classification for fault diagnosis of rotating machines. In this paper, we study these three predictive models to predict the surface roughness based on the vibration signals in the milling process. For a machine monitoring system used in the milling process, measuring vibration signals based on sensory data is a characteristic task. A conventional model prediction method, such as neural network and SVM, cannot express the sequential features extracted from serious vibration signals. LSTM can deal with the different lengths of data in sequential time and extract long-term series features for postprocessing. The deep training structure is capable of recognizing unseen data and thus can be used to generalize the predictive model. Although LSTM considers sequential data characteristics, some shortcomings of the model may not allow it to achieve robust prediction based on raw sensory data. As suggested by [PERSON] et al. [37], the raw data converted from time-domain signals to frequency domain spectrum features are fed into the LSTM to achieve high performance in prediction. In addition, a report in the literature [38] showed that CNNs, LSTMs, and Deep Neural Networks (DNNs) have respective prediction capabilities: CNNs are capable of filtering signal noise by convolutional filters and pooling operations, LSTMs can deal with temporal modeling, and DNNs are appropriate for mapping features in multidimensional space. The objective of this research is to study the Fast Fourier Transform (FFT) extractor and the one-dimensional convolutional extractor combined with three predictive models, namely, FFT-DNN, FFT-LSTM, and 1-D CNN, to predict the surface roughness via vibration signal information. This paper is organized as follows: Section 2 presents details on the research methodology and describes the structure of each of the models.