## 4 Experiment and Verification
### Model Verification  
The experimental program was a combination of L\({}_{9}\)(3\({}^{4}\)) orthogonal test, as well as a supplementary experiment, as shown in Table 2. Supplementary experiments 4, 5, 6, 6a, 6b, 8, 9, 10, 10a, 10b, 15, 16, 17, 17a, and 17b were placed behind 3, 7, and 14 respectively with the same milling parameters to 3, 7, and 14. The orthogonal test and supplementary experiment based on the test were arranged to provide orthogonal data for the statistical analysis and verification. As shown in Table 2, the milling width was \(a_{e}=0.15\) mm and the milling length unit was \(L_{u}=16\),588.5 mm. \(L_{u}\) is the minimum length at which the tool displays significant measurable flank wear under the minimum cutting parameters. During the experimental trial cut phase, as shown in Figure 12, the smallest surface contained a total unit milling length \(L_{u}\) under constant \(a_{e}\). Additionally, the entire surface was divided to facilitate the study of wear and surface roughness accumulation. The milling sequence was also disrupted for grouping of milling lengths, as well as avoiding systematic errors to some extent. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**G** & **Tool Code** & \(n\) **(t/min)** & \(a_{p}\) **(mm)** & \(f_{z}\) **(mm)** & \(L\) **(mm)** \\ \hline  
& 12 & 4500 & 0.7 & 0.14 & 48 L\({}_{u}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental program. Figure 11: Wear measurement method and result. The surface roughness of the test piece was detected by [PERSON]. A CCI white light interferometer and the measurement position and area are shown in Figure 12. As shown in Figure 10, the surface was divided into multiple segments, and the measurement points were located at 1/3 and 2/3 of each surface. For the comprehensiveness and accuracy of the \(R_{a}\) measurement, areas \(a\) and \(b\) were set in one measurement, and each of them respectively contained a pair of affiliated measurement areas for taking averages [36]. Finally, each sub-surface's roughness is represented by the average of areas \(a\) and \(b\). To measure the wear, the total time for the tool to travel over a certain surface needs to be recorded. Thus, a new tool must be used for each group of experiments. After milling, the wear of the measurement position was estimated by the final wear of this group, as well as the time recorded. Normal wear is a slow process, and previous trial milling experiments have proven the feasibility. As shown in Figure 13, with the exception of a few obviously inaccurate measurement points, the values and distribution of the experimental values, as well as the predicted curves, are consistent. As the prediction model ignored some undesirable cutting phenomena, such as the built-up edge and excessive vibration, the experimental values are generally larger than the predicted values. As time progresses, the tool wear gradually increases, and this error will be amplified. Because some influencing factors were simplified or set as constant during modeling, the parameters were divided into two main categories. The first is the model coefficients obtained by reference to the manual and the literature, such as \(a\), \(b\), \(c\), and \(d\) in abrasive wear, \(e\) in adhesive wear, and \(g\) in the diffusion wear shown by (12). The second category is the parameters corrected by conducting research experiments, such as, \(K\), \(C\) in (9), \(K_{0}\), \(C_{0}\) in (11) and \(K_{1}\), \(K_{2}\), and \(K_{3}\) in (12). They are both parameters that characterize the materials of the tool and workpiece during processing, but, in fact, they all change with the processing rather than remain constant. The influence of these  
Figure 12: Measuring surface roughness. Figure 13: Value comparison of experimental and model data. factors expands due to the accumulation of wear. Consequently, this leads to the experiment entering the accelerated wear period earlier than the model.