## 1 Introduction  
Surface roughness is regarded as a significant indicator of integrity for cutting surfaces and has attracted widespread attention in the most recent decades. Nevertheless, due to the lack of understanding of the cutting mechanism, in practice, the desirable surface roughness in the processing process remains, to a large extent, reliant on human experience. The surface roughness can also be made adjustable by special processing methods, such as low temperature cutting and heat cutting, but these special processing methods can cause a processing distortion. In contrast, the processing error resulting from the combination of different processing parameters is significantly smaller than if the special processing method is adopted [1]. Not only does the unreasonable selection of process parameters or tools increase production costs, it also causes the surface quality to deteriorate. The addition of the artificial intelligence model makes it possible to construct a nonlinear model of the process parameters and response values quickly. Besides, it facilitates the optimization of the process parameters to satisfy the production efficiency requirements of the enterprise, thereby achieving the control of the surface quality and improvement of the service life of the parts [2]. Moreover, the relatively high precision prediction model plays a significant role in exploring the influence exerted by innovative tools and new processes on the surface integrity [3]. At present, various advanced modeling techniques and simulation methods have been widely applied in material processing, such as multiple regression [4], response surface method (RSM) [5], convolutional neural network (CNN) [6], neural network (ANN) [7], support vector machine (SVM) [8], and adaptive neuro-fuzzy inference system (ANFIS) [9], etc. By applying Grey-Taguchi-based response surface methodology (GT-RSM), [PERSON] et al. [10] optimized the plasma arc cutting parameters of 304 stainless steel. When the coated cemented carbide tool was employed to process (HSM) stainless steel at high speed, [PERSON] et al. [11] adopted the Taguchi optimization method to obtain a minimum cutting force and achieve excellent surface roughness. Besides, they conducted an analysis of the optimization process with the assistance of both the signal to noise ratio response analysis (S \(\leq\) N) and Pareto variance (ANOVA). Nevertheless, such methods can only be capable of achieving better accuracy with a small range of process parameters [12; 13]. [PERSON] et al. [14] employed the response surface method to predict the surface roughness and cutting force of AQL4340 under the micro-lubrication of nanofluids. [PERSON] et al. [15] constructed a multivariate regression model of surface roughness, which has a maximum error of 7.45% when surface roughness is predicted. Polynomial-based regression methods have the capability of quantifying the relationship between input parameters and response variables when a nonlinear relationship between the cutting parameters and surface roughness is established [16]. However, in recent years, the supervised learning based methods have achieved higher desirable accuracy and better reliability for surface roughness prediction modeling [17]. [PERSON] and [PERSON] [7] proposed an average surface roughness prediction model that is premised on artificial neural networks and revealed the effect exerted by the hard turning of a high pressure coolant on surface roughness. [PERSON] et al. [18] conducted a combination of fuzzy logic and regression analysis to examine the influence exerted by machining parameters on the surface roughness of milling. [PERSON] et al. [8] supplemented the cutting energy consumption (SCEC) and took PSO-SVM as a prediction model, on the basis of which a high prediction accuracy was achieved. [PERSON] et al. [17] performed a comparison of the ability between three machine learning methods (SVR, multiple regression, and ANN) in the process of high speed turning.