## 1 Introduction  
The material of Inconel 718 has been broadly used in manufacturing the components of aircraft and space vehicles in the aerospace industry. In the milling process of Inconel 718, the surface roughness is one of the important indicators to assess the work-piece quality. The surface roughness of work-piece is mainly influenced by the combinations of cutting parameters, tool machine vibrations, tool wearing and other external factors. In order to improve the surface condition of work-piece and enhance the manufacturing efficiency, the essential tasks involve the accurate prediction of surface roughness quantities as well as the optimization of the cutting parameter combinations. The traditional studies indicated that the surface roughness of work-piece is dominated by the cutting speed, feed rate and cutting depth [1]. Therefore, the different combinations of cutting parameters were utilized to predict the surface roughness of work-piece in the machining process through the artificial neural network (ANN) and regression scheme [1, 2]. As the concept of intelligent manufacturing was proposed, the vibration measurements of machining process were utilized to improve the prediction accuracy of surface roughness. [PERSON] and [PERSON] [3] presented a semisupervised approach to the development of roughness prediction models in the milling process. In their study, the vibration data are used in combination with the K-nearest Neighbors and random forest techniques to increase the accuracy of the prediction models. Alternatively, the classifier of support vector machine was employed to predict the surface roughness of aluminum plate during the end milling process through the feature sets that include the statistical parameters, FFT spectra and wavelet packets [4]. In addition, a prediction model based on energy consumption was proposed to estimate the surface roughness in turning operation [5]. The cutting energy consumption and cutting parameters were extracted as the inputs to the prediction model in their research. [PERSON] et al. [6] conducted the research to develop the predictive model through evaluating the tool vibration acceleration and the surface roughness of AISI 52100 steel with different machining parameters. Their investigation indicated that the prediction of ANN is more accurate than the regression analysis. A series of artificial intelligence methods, including random forest, standard multilayer perceptrons, regression trees and radial-based functions, are tested to predict the surface roughness deviation through taking the tool wear into account [7]. Traditionally, the selection of cutting parameters in the machining process is mainly based on the Taguchi method. [PERSON] [8] presented a cutting parameter optimization approach to minimize the cutting energy consumption and surface roughness of AISI 6061 T6 during the turning operation through the Taguchi method. The results of the research work showed that the feed rate is the most significant factor for minimizing energy consumption and surface roughness. Owing to the rapid development of artificial intelligence techniques and optimization algorithms, several approaches have been applied to search the optimal combination of cutting parameters for different machining conditions. [PERSON] et al. [9] employed the multiperceptron ANN to build the surface roughness model and used the artificial immune algorithm to find the optimal parameters, including cutting velocity, feed per tooth and cutting depth, to minimize the surface roughness in the milling process of Ti-6A1-4V. Additionally, the particle swarm optimization (PSO) technique was utilized to find the optimal cutting parameters for minimizing the face milling time under the desired surface roughness values of aluminum work-piece based on the model that depicts the relationship among the cutting velocity, feed rate, cutting depth and surface roughness [10].