# System Design and Monitoring Method of Robot Grinding for Friction Stir Weld Seam
## 3 Result
### Experiment of Monitor of Grinding Process Based on PSO-SVM Method
#### 3.4.3 Result Comparison  
In this study, sample acquisition is difficult and laborious. Each sample acquisition requires robotic scanning of the weld seam, welding seam analysis, robot grinding, and evaluation of the grinding results. Therefore, in the selection of comparison methods, methods such as deep learning of large samples cannot be selected. At the same time, in the current research for grinding monitoring, most of them are grinding for flat planes or curved surfaces, and many types of research are done by modeling and identifying parameters. Due to the different grinding objects in our system, this paper cannot adopt the model-based method. The input parameters include the point cloud data and dynamic force data, which are different from the existed grinding monitor method. Therefore, this system is not applicable to traditional monitoring methods and cannot be compared.  
Therefore, the experimental comparison includes two parts. The first comparison is the classification results with and without point cloud data. In the second comparison, many traditional small sample classification methods are adopted.  
In the first comparison, A total of 1000 experiments were conducted. In each experiment, from 101 samples, 20 samples were randomly selected as the test set, and other samples were used as the training set. Two methods are used respectively: full input feature method (seven input features) and no point cloud feature value method (five input features) for training and verification. The result is shown in Figure 19. In the figure on the left, the value around each vertical line is the number of experiments at this accuracy. It can be seen that the method of using seven inputs has significantly higher accuracy,  
Figure 17: PSO algorithm result.  
Figure 18: Classification result.  
and its accuracy is concentrated near 90%. The results of the five-input method are concentrated around 85%. The average accuracies of the two methods are 91.515% and 84.315%. Therefore, it can be concluded that the point cloud feature plays a role in the monitoring method, and can improve the accuracy of the excessive grinding prediction.  
In the second comparison, some traditional classification algorithm results are shown in Table 1. In the table, for each method, 1000 experiments are conducted, which is the same as those in the first comparison. The average accuracy of the 1000 results is shown in the table. It can be seen that the adopted method is better than other methods. The reason is that through the optimization of the PSO method, the effect of the SVM classifier is better, and a better classification accuracy can be achieved. Since the training is conducted offline, after the training is completed, in each online monitoring, the time taken to classify the sample is within 1 ms, which is negligible compared to the feature calculation time in Section 3.4.2. Therefore, in terms of calculation time, this method has the same effect as other methods and has no effect on practical applications.