In the field of ultrasonic array imaging for non-destructive testing (NDT), material structural noise caused by grain scattering is one of the main sources of error when characterising defects that are found in polycrystalline materials. The existence of grains can also severely affect the detection performance of ultrasonic testing, making small defects indistinguishable from the grain indications due to ultrasonic attenuation and backscatter. This paper proposes a model in which the statistical distribution of the defect data is obtained from different realisations of the grain structure. This statistical distribution, termed the defect+grains model in this paper, is shown to contain information that is needed for detection and characterisation of defects. Hence, given a specific measurement configuration, the characterisation result can be obtained by constructing a defect+grains model based on multiple realisations of each possible defect and calculating their probability. The detection, classification, and sizing accuracy are shown to be predictable by quantifying the probabilities that an experimentally measured defect matches the different defect+grains models. This defect+grains modelling approach gives insight into the detection/characterisation problem, leading to an evaluation of the fundamental limits of the achievable inspection performance.
|Number of pages||16|
|Journal||IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control|
|Publication status||Published - 8 Jul 2019|