Grain Scattering Noise Modeling and Its Use in the Detection and Characterization of Defects Using Ultrasonic Arrays

Abstract

In the field of ultrasonic array imaging for nondestructive 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.

Design and Application of Partial Immersion Focused Ultrasonic Transducers for Austenitic Weld Inspection

“…When the scanning experiment is performed and reference signals are measured, the distance between the PIF transducer and the weld is made as large as possible. The detailed theory used to determine the time-dependent threshold can be found in published works [ 21 , 23 ].…”

“…When the weld is scanned using automatic detection, an ultrasonic scan image is usually provided for flaw detection and evaluation. It has been shown that noise information can be obtained from grains or the boundaries of different weld zones [ 21 , 22 ]. It is hard to judge the flaw signal in the image unless one knows of the existing flaws in advance.…”

A precise bare simulation approach to the minimization of some distances. Foundations

“…There is a vast literature on very recent applications of the Bhattacharyya coefficient, for instance it appears exemplarily in Peng & Li [289] for object tracking from successive video frames, Ayed et al [26] for efficient graph cut algorithms, Patra et al [287] for collaborative filtering in sparse data, El Merabet et al [119] for region classification in intelligent transport systems in order to compensate the lack of performance of Global Navigation Satellites Systems, Chiu et al [86] for the design of interactive mobile augmented reality systems, Noh et al [274] for dimension reduction in interacting fluid flow models, Bai et al [29] for material defect detection through ultrasonic array imaging, Dixit & Jain [115] for the design of recommender systems on highly sparse context aware datasets, Guan et al [143] for visible light positioning methods based on image sensors, Lin et al [220] for probabilistic representation of color image pixels, Chen et al [80] for distributed compressive video sensing, Jain et al [162] for the enhancement of multistage user-based collaborative filtering in recommendation systems, Pascuzzo et al [285] for brain-diffusion-MRI based early diagnosis of the sporadic Creutzfeldt-Jakob disease, Sun et al [351] for the design of automatic detection methods multitemporal (e.g. landslide) point clouds, Valpione et al [377] for the investigation of T cell dynamics in immunotherapy, Wang et al [387] for the tracking and prediction of downbursts from meteorological data, Xu et al [403] for adaptive distributed compressed video sensing for coal mine monitoring, Zhao et al [424] for the shared sparse machine learning of the affective content of images, Chen et al [82] for image segmentation and domain partitioning, De Oliveira et al [105] for the prediction of cell-penetrating peptides, Eshaghi et al [122] for the identification of multiple sclerosis subtypes through machine learning of brain MRI scans, Feng et al [125] for improvements of MRI-based detection of epilepsy-causing cortical malformations, Hanli et al [153] for designing pilot protection schemes for transmission lines, Jiang et al [170] for flow-assisted visual tracking through event cameras, Lysiak & Szmajda …”

“…where P emp n was defined in the course of (29). Such m n (Q) is well defined for all Q since it satisfies the equation (in m)…”

A study of supervised combined neural-network-based ultrasonic method for reconstruction of spatial distribution of material properties

“…Metal aging in most cases leads to changes in the structure of the materials such as the appearance of microcavities, cracks, which later can lead to the development of bigger damages of the structure, and as consequence, the materials lead to destruction. To examine the metals with non-destructive testing methods, the detection of the changes in noisy material internal structure is a very challenging task [2]. Even non-destructive assessment of the grain size inside the metals in principle is not solved.…”