## 8 Conclusions  
In this paper, seven feature parameters in the time domain, frequency domain, and time-frequency domain are analyzed and selected according to two proposed feature parameter selection standards and put into the SVM model to measure the angle and depth of oblique surface cracks. To explore the feature parameter selection standards, the correlation coefficients between crack geometry information and feature parameters and between the selected feature parameters themselves are analyzed. To verify this SVM model, a 2D FEM model is built to acquire ultrasound signals to extract seven feature parameters and subsequently include them in an SVM model. Finally, comprehensive verification by FEM simulation and experimental data is conducted.  
The conclusions are as follows:  
1. The five-parameter SVM model, seven-parameter SVM model, and nine-parameter SVM model are compared using the FEM. The best prediction results are obtained using a seven-parameter SVM model. The results also show that the selected feature parameters in this paper are suitable for the SVM model to nondestructively detect surface crack depths and angles.
2. This paper shows that the SVM method is suitable for laser ultrasound to quantitatively detect oblique surface cracks, and its advantages are especially revealed when the amount of training set is small sample data. All of the training sets used in this paper satisfy the definition of a small sample.
3. An intelligent detection method using the SVM model is provided by both FEM simulations and the experimental method. When putting the model established in this paper into actual detection, its accurate classification rates are 95.5% for angles and 83.3% for depths, which demonstrates the feasibility of the SVM model based on laser ultrasonics for practical surface crack detection.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12168124/s1](https://www.mdpi.com/article/10.3390/app12168124/s1)]([https://www.mdpi.com/article/10.3390/app12168124/s1](https://www.mdpi.com/article/10.3390/app12168124/s1)).  
Conceptualization, Y.L.; Data curation, J.D.; Funding acquisition, H.L.; Investigation, Z.A. and Q.P.; Methodology, H.L.; Project administration, H.L.; Software, Y.L.; Writing--original draft, Y.L.; Writing--review & editing, H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China as National Major Scientific Instruments Development Project, grant number 61927807 and National Natural Science Foundation of China, grant number 11874386, supported by Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi, the Opening Project of Key Laboratory of Biomedical Imaging and Image Data of Shanxi Province, KF2020-04, the Opening Project of Shanxi Key Laboratory of Advanced Manufacturing Technology, grant number XJZZ202006 and the Opening Foundation Project of Nanchang Hangkong University Key Scientific Research Base, grant number EW202180221.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the Supplementary Material. The data presented in this study are available in supplementary data.rar.  
**Conflicts of Interest:** The authors declare no conflict of interest.