## 4 Model Prediction Results and Analysis
### Multi-Objective Optimization of Cutting Parameters Based on GA_Gbrt  
Surface roughness makes a major impact on the surface of the part, including wear resistance, fatigue strength, lubrication, and friction, as well as optical properties. The involvement of artificial intelligence models can be quick to construct relevant high-precision prediction models. Besides, it is conducive to optimizing process parameters so as to ensure enterprise production. Efficiency requirements can also be met to control the surface quality and extend the service life of parts. The higher the model accuracy, the better the reliability of the optimized surface roughness value. Genetic algorithm is considered as ideal for the field of cutting parameter optimization due to its excellent global optimization ability. In order to determine the minimum surface roughness of the cutting parameters, the GA-GBRT model is simplified to \(R_{a}=f(v,f,a_{p})=f(x)\). In addition, the optimized variable European space is \(x=(v,f,a_{p})\in E^{3}\). Thus, the variable value is:  
\[R_{1}=\Big{\{}x\in E^{3}\Big{|}90\leq V\leq 210,0.05\leq f\leq 0.18,1\leq ap\leq 2 \Big{\}}. \tag{13}\]  
The maximum target for material removal rate is \(MRR=1000\cdot v\cdot f\cdot a_{p}\), then the multi-objective optimization function is  
\[\left\{ \begin{array}{l}\min(Ra)=f_{1}(v,f,a_{p})=f_{1}(x)\\ \max(MRR)=f_{2}(v,f,a_{p})=f_{2}(x)\\ x\in E^{3}\end{array} \right.. \tag{14}\]  
With the highest cutting efficiency and the best surface quality regarded as the optimization problem, the multi-objective optimization function based on genetic algorithm is applied to solve the model. As shown in Figure 14, through the Pareto optimal front change curve, it can be seen that the overall trend is that, with the rise of the material removal rate, the roughness value will increase on a continued basis. In area A, the material removal rate increases significantly with a rise in the roughness. Furthermore, the lower roughness can be obtained in this area, and the surface precision of the workpiece is improved. Nevertheless, the material removal rate is excessively low, resulting in an  
Figure 13: Performance comparison of five surface roughness prediction models: (**a**) MAD; (**b**) MAPE; (**c**) RMSE; (**d**) CV. overly low cutting efficiency. The recommended parameter is shown in area A of Table 7. In region C, the material removal rate changes at a relatively slow pace, despite the roughness growth being accelerated. In this region, a high material removal rate can be maintained, thereby achieving a high cutting efficiency. Despite this, the roughness value is large, and the recommended parameter is shown in area C of Table 7. In region B, as the roughness increases, the material removal rate rises linearly. In addition, the surface roughness and material removal rate reach the desired value simultaneously. The recommended parameters are shown in area B of Table 7.  
The optimal solution to the three variables in the three regions A, B, and C is shown in Table 7. Combined with the main effect analysis in Figure 15, a conclusion can be drawn that the cutting speed exerts little effect on the surface roughness, when it affects the surface quality. In case the surface roughness is at low levels, the higher cutting speed is selected to ensure the highest cutting efficiency. The influence exerted by the cutting depth on the surface roughness is similar to the cutting speed, which ensures that the surface quality is within a small range of variation and increases the cutting depth to improve the cutting efficiency.