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This study is interested in extracting representative features of the ultrasonic phased array image intelligent classification of the internal defects of naval gun mounts. Thus, an improved sparse self-encoding network model (RSAE) is proposed to realize the re-expression of sample data. First of all, in intelligent classification, the deterministic initial weight will lead to the best or worst result of neural network training, but in complex problems, it is very likely to get the worst result; at the same time, the neural network uses random weights. The results of training multiple times fluctuate greatly, which is not conducive to the performance evaluation of the network model. Therefore, this paper does not directly use the correlation parameter between the feature and the defect category as the initial feature weight of the RSAE. Instead, given a cell, the correlation parameter between the feature and the defect category is located in this cell. Then, on this basis, the optimization goal is to minimize the reconstruction error of training sample data, minimize the deviation of similar sample data, and maximize the difference of sample data between classes to realize the re-expression of sample data. The experimental results show that the advanced features obtained by the improved sparse autoencoder proposed in this paper are better than the original features in pattern recognition. This network can be used to more accurately identify the types of internal defects in the welds of naval gun mounts.

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This study is interested in the normal calculation of artillery cradle. When the beam axis of ultrasonic phased array probe cannot detect the workpiece surface vertically, the detection result is usually inaccurate. Thus, in order to determine the normal of the surface to be detected, a method based on visual point cloud to calculate the normal of artillery cradle is proposed in this paper. The proposed method solves two key problems. One is to sparse the point cloud while preserving the tiny information on the surface. A mesh division method along the uv direction is proposed. The disordered point cloud data in 3D space are connected into regular polygon meshes. While establishing the local relationship of point cloud data, it also maintains the overall integrity of point cloud data. The other is to calculate the normal vector of the point cloud. The local plane fitting method based on the least square method calculates the normal at a point of the point cloud. At this time, the calculated normal are nondirective. When there is no abrupt curvature change near the calculated point cloud, the direction of the normal is determined based on the same slope at two adjacent points. The calculated point cloud normal vector is compared with the actual normal vector of the mathematical model. The maximum deviation between the theoretical value and the actual value of the normal vector is 0.465°, and the average deviation is 0.302°. The experimental results show that the proposed method can be used to detect the actual normal of artillery cradle.