Classification Method of Internal Defects in Metal Materials Based on Ultrasonic Point Cloud Data

Abstract

Abstract The commonly used methods for analysing defects in metal materials include metallography and scanning electron microscopy(SEM). However, these methods require grinding and polishing of the material surface, which can only obtain the characteristics of material surface defects, making it difficult to effectively detect and characterize internal defects in metal materials. Therefore, a deep neural network model based on ultrasonic point cloud data was proposed in this paper, called the Global and Local Feature Fusion Network (GLFFN), to classify the internal defects in metal materials. Firstly, an ultrasonic microscope was used to perform X-layer scanning on the metal materials, obtaining ultrasonic images at different depths, from which ultrasonic point cloud data was extracted. Based on this, the GLFFN model enhances the classification accuracy of internal defects in metal materials by deep fusion of global and local features of the point cloud. With casting billets as the detection object, which contain three typical internal defects: inclusions, shrinkage cavities, and cracks. The accuracy of the new model for defect classification reached 88.24%, which is superior to the traditional methods, proving the effectiveness of the new model on the classification of internal defects in metal materials.