## 1 Introduction
[27] proposed a visual roughness measurement method based on transfer kernel learning for the purpose of solving the roughness observation problem of the grinding surface. Surface roughness prediction and optimization presents a new challenge when high hardness steels are turned under minimum coolant lubrication conditions. [PERSON] et al. [28] addressed this problem using the least squares support vector machine (LS-SVM) method. [PERSON] et al. [29] integrated six different machine learning algorithms to predict the surface roughness of three-dimensional printed samples. In recent years, artificial intelligence has been applied to accurately perform analyses and explore various new processes and technologies, due to its superior performance in constructing a relational model of input and output. In the machining of the 304 stainless steel toughness material, the soft Ferrite phase will cause the tool-chip contact length of the rake tool surface to be longer, thus resulting in more friction. The sharp rise in temperature severely affects the service life and the surface quality of workpiece [11]. A reasonable control of the contact characteristics of the rake tool surface is conducive to improving the service life of the tool and the quality of machined surface [30]. The newly designed micro-groove tool is based on the temperature field morphology. A new micro-groove is innovatively added to the original commercial groove tool, and has a smaller scale than that of the ordinary groove type. As demonstrated by previous research and experiments on the machining of a variety of materials, micro-groove tool can be effective in reducing the cutting temperature, changing the distribution of the cutting energy, and improving the service life of the tool and machining efficiency [31; 32]. The complex rake face shape changes the stress state of the deformation zone, which can reduce the plastic deformation energy (EPD) and potentially enhance the machinability (PMA). As indicated by [PERSON] [33], the geometric shape of the tool can be designed with a high performance with the assistance of the finite element model of metal cutting. The design concept of the micro-groove tool in this paper conforms to the method proposed by [PERSON]. However, for a tool of a certain shape, when one or more of the machining conditions are changed, the tool that originally produced desirable test results may not be capable of performing satisfactorily under certain machining conditions. Therefore, for new processes and new tools, the analysis of the impact made by the parameters and reasonable setting of the cutting parameters can achieve good surface quality and high material removal rate within a certain range [13]. To figure out the impact made by the self-developed micro-groove cutter on the surface roughness of 304 stainless steel and guide the selection of cutting parameters, in this paper, a surface roughness prediction model based on GA-GBRT model is proposed. Firstly, the initial parameters of the model are optimized by combining genetic algorithms to enhance the generalization ability of GBRT. Secondly, a comparison is drawn with the existing ANN and SVR model prediction results. The comparison results confirm the validity of the GA-GBRT prediction roughness. Finally, based on GA-GBRT, the genetic algorithm multi-objective optimization function is involved to solve the optimization model, and the impact of the cutting parameters of the micro-groove cutting AISI 304 on the average surface roughness is investigated.