Scattering matrix similarity metric optimization for improved defect characterisation based on dynamic graph attention networks

Abstract

Ultrasonic scattering matrices contain rich defect information and have great potential for characterising small crack-like defects. However, experimentally measured scattering matrices often exhibit some level of distortions compared to those of the idealised defects, posing challenges for accurate defect characterisation. In this paper, defect characterisation was performed by adopting a nearest neighbour approach based on a scattering matrix database of reference defects, and the test data were contaminated by coherent measurement noise of varying amplitudes. The performance of different similarity metrics on characterisation accuracy was studied, including the Euclidean similarity, cosine similarity, Pearson correlation coefficient, and the structural similarity index. Based on a comprehensive analysis of the strengths and weaknesses of different similarity metrics, we propose a defect characterisation framework by constructing similarity graphs and leveraging advanced graph neural networks. Within the proposed approach, multiple metrics were adopted to quantify the similarity between the scattering matrices of different defects, and an improved dynamic graph attention network was developed based on a customised neighbour sampling strategy to learn the optimal metric from the graph-structured data. Experimental results show that compared to the conventional approach which adopted a globally optimal similarity metric, the proposed method can reduce the root mean squared error for the length and angle predictions by 60.5% and 67.1%, respectively.