## 6 Simulation Evaluation Results
### Experimental Result Discussion  
Based on the above experimental platform, the 3D and 2D plots of the B-Scan images of oblique cracks with angles of 60\({}^{\circ}\), depths of 0.5 mm, and widths of 0.2 mm are shown in Figure 21.  
In Figure 21, the direct wave, transmitted wave, and reflected wave are labeled. There is a significant reduction in amplitude when a direct wave interacts with an oblique crack. Part of the direct wave reflected by the crack forms the Rr wave, and when the crack approaches the laser source, the Rr wave is gradually generated near the direct wave. The  
Figure 21: Three- and two-dimensional plots of the B-Scan image of oblique cracks (angle = 60\({}^{\circ}\), depth = 0.5 mm). Figure 20: Schematic block diagram of the experimental platform. In Figures 24 and 25, the normalized feature parameters of the experimental data are denoted by dots, and the fitting lines are denoted in red. The determination coefficients of the fitting curves in Figures 24 and 25 are 0.842 and 0.771, respectively. The fitting curve of the angles has a better linear relation than that of the depths. Moreover, the distance from the data of crack depth at 0.3 mm to the fitting curve is 0.11772, which is larger than the other two data of the crack depth at 0.4 mm and 0.5 mm at the distances of 0.05528 and 0.09352, respectively, as shown in Figure 25. Then, the evaluated crack angles and depths of the tested specimens are shown in Figures 26b and 27b and compared to the actual sequence in Figures 26a and 27a. Figure 26a shows the actual angles versus 22 data points using stems. Both 1-11 data points and 12-22 data points are two test sets denoting 11 angles at 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 70\({}^{\circ}\), 80\({}^{\circ}\), 90\({}^{\circ}\), 100\({}^{\circ}\), 110\({}^{\circ}\), 120\({}^{\circ}\), 135\({}^{\circ}\), and 150\({}^{\circ}\). After the angle prediction using the SVM model, Figure 26b shows the evaluated results for two test sets at angles of 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 70\({}^{\circ}\), 80\({}^{\circ}\), 90\({}^{\circ}\), 100\({}^{\circ}\), 110\({}^{\circ}\), 120\({}^{\circ}\), 135\({}^{\circ}\), and 150\({}^{\circ}\). By successively comparing the value at each data point in Figure 26a,b, we obtain the errors between actual angles and evaluated angles as presented in Figure 28a. The prediction errors only occur at an angle of 110\({}^{\circ}\) at the 19 th data point, which belongs to the second test set. The SVM model using seven feature parameters in this work trains 88 samples into 11 angles. Its accurate classification rate is 95.5% for experimental data. Highly accurate angle measurements using the SVM based on laser ultrasonics are obtained for inclined surface cracks. Figure 24: Experimental data and fitting line of the normalized feature parameter versus angles. Figure 25: Experimental data and fitting line of the normalized feature parameter versus depth. Figure 28: (a,b) prediction errors. Figure 27: (a) Actual depth sequence; (b) prediction depth sequence. In Figure 27, 12 data points are shown using stems for the actual depth (a) and evaluated depth (b). Here, data points 1-6 and data points 7-12 are one test set, which is described at 0.1 mm, 0.2 mm, 0.3 mm, 0.4 mm, 0.5 mm and 0.6 mm. Similar to the angle prediction process, two test sets are used for the depth measurement. The prediction errors between the actual depths in 27(a) and the evaluated depths in 27(b) are shown in Figure 28(b), where the measurement errors occur at the third data point and ninth data point, which are the depths of 0.3 mm. The evaluated error is 0.1 mm. The SVM model set using seven feature parameters in this work trains 48 samples that are classified into six depths. The accurate classification rate is 83.3%, which is lower than the accuracy rate of the angle classification.