Semi-Automatic Defect Distinction of PAUT for High Strength Steel Corner Structure

Abstract

Abstract In this study, A GCN distinction network is proposed to identify the defect status of steel corner structures. In this defect determination method, the detected S-scan data is converted into A-scan by Doppler software to form a two-dimensional detection matrix, and the two dimensions are deflection angle stepping and sampling point on the acoustic distance respectively. GCN needs data node relations for GCN’s adjacent matrix, which concludes the shape of the sample and the reflection information and the structure of acoustic beam direction. The conventional inverse and threshold method are introduced in order to enhance classification performance. According to the node and the adjacency matrix relationship between nodes of affiliation and node sampling value automatically set the node label, The GCN model is constructed. Due to the characteristics of GCN, the beam length in the adjacent matrix cannot be changed again in the application test. Except for the detection model of Beamtool software, all other data processes and distinction are carried out automatically. In this study, the PAUT of low-alloy steel (30CrMnSi) was carried out by the PHASCAN II detector, to verify the effectiveness of the research method through natural wave and defect echo experiments which will be confused to distinguish.