# Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning
## 4 Results and Discussion
### Surface Roughness  
Due to the high hardness and toughness of the SKD11 tool steel, various issues may arise during the machining process. Different assisted methods and machining parameters are crucial factors affecting machining performance. Table 1 shows the planning of process parameters in the first phase of the experiment, while Figure 10 shows the relationship between surface roughness and process parameter combination in this phase without assistance and with different ultrasonic-assisted methods. Here, the surface roughness parameter measured is Ra, and its measurement device is a contact-type surface roughness-measuring instrument (Mitutoyo SJ-410). The experimental results indicate that a small depth of cut and a slow feed rate result in better surface roughness than a large depth of cut and a fast feed rate, both without assistance and with different ultrasonic-assisted methods. Among them, the rotary ultrasonic-assisted method proves superior to both the absence of assistance and the other ultrasonic-assisted methods. However, the absence of assistance yields better performance than both dual-axis and triple-axis ultrasonic assistance. It is speculated that dual-axis and triple-axis ultrasonic assistance, which involve radial vibration, are less effective in removing material due to the high toughness property of the workpiece material. This deduction suggests poorer performance. Compared to no assistance, rotary ultrasonic assistance can reduce surface roughness by approximately 5%. Therefore, the effects of both no assistance and different ultrasonic-assisted methods on the surface roughness of SKD11 milling are investigated, and the best assisted method is ascertained. This superior ultrasonic-assisted method, rotary, will be applied in the second phase milling experiments.  
The superior ultrasonic-assisted method obtained from the first phase is adopted in the second phase in conjunction with and without laser locally preheating assistance, hybrid-assisted, and rotary ultrasonic-assisted, respectively, in which the process parameter planning for the milling experiment on SKD11 is shown in Table 2. An ANOVA (analysis of variance) is performed on the sampling data to examine the influence and contribution of various factors to surface roughness. The contribution rates are shown in Tables 5 and 6, and the corresponding factor responses are illustrated in Figure 11a,b, respectively. It can be observed that under both of these assisted methods, the most significant factor affecting the surface roughness of SKD11 milling is the feed rate, followed by radial depth of cut, and lastly spindle speed. The experimental process parameter design for milling experiments  
Figure 10: The relationship between surface roughness and process parameter combinations in the 1 st phase experiment without assistance and with different ultrasonic-assisted methods.  
through L9 orthogonal arrays is presented in Table 2, and the corresponding experimental sets are shown in Table 3. Comparing surface roughness between hybrid-assisted and rotary ultrasonic-assisted methods for process parameter combinations in the L9 orthogonal array, as shown in Figure 12, it is evident that the hybrid-assisted method can reduce surface roughness by up to 30%, compared to rotary ultrasonic-assisted alone. Among them, lower feed rates result in better surface roughness. Higher feed rates may accelerate the cutting progression but may cause vibrations between the cutting edge and the workpiece surface, leading to deteriorated surface roughness. Meanwhile, a slower feed rate allows for an increase in laser preheating time during the cutting processes, effectively achieving a sufficient preheating phenomenon, and thereby enhancing the surface roughness of the machined surface.