Abstract
The aim of ultrasonic non-destructive evaluation includes the detection and characterization of defects, and an understanding of the nature of defects is essential for the assessment of structural integrity in safety critical systems. In general, the defect characterization challenge involves an estimation of defect parameters from measured data. In this paper, we explore the extent to which defects can be characterized by their ultrasonic scattering behaviour. Given a number of ultrasonic measurements, we show that characterization information can be extracted by projecting the measurement onto a parametric manifold in principal component space. We show that this manifold represents the entirety of the characterization information available from far-field harmonic ultrasound. We seek to understand the nature of this information and hence provide definitive statements on the defect characterization performance that is, in principle, extractable from typical measurement scenarios. In experiments, the characterization problem of surface-breaking cracks and the more general problem of elliptical voids are studied, and a good agreement is achieved between the actual parameter values and the characterization results. The nature of the parametric manifold enables us to explain and quantify why some defects are relatively easy to characterize, whereas others are inherently challenging.
Highlights
The aim of ultrasonic non-destructive evaluation and structural health monitoring includes the detection and characterization of defects [1,2,3,4,5], and an understanding2017 The Authors
Given a number of ultrasonic measurements, we show that characterization information can be extracted by projecting the measurement onto a parametric manifold in principal component space
A new defect characterization approach which can potentially be applied to any defect databases has been proposed in this paper
Summary
The aim of ultrasonic non-destructive evaluation and structural health monitoring includes the detection and characterization of defects [1,2,3,4,5], and an understanding. A regularization procedure is required in general inversion approaches [18] in order to deal with the ill-posedness of the studied problem, and issues including numerical stability and convergence need to be addressed [19] This has led researchers to consider more targeted approaches, and the second approach makes use of the fact that there is only a limited number of possible defect types that can occur in practice. Their use can be found in a range of applications, including radar target recognition [25], underwater target classification [26], classification of electroencephalogram signals [27] and in bioinformatics (e.g. gene selection for cancer classification [28]) Using this classification algorithm approach to the characterization problem, good results have been achieved on simulated data and in idealized experiments containing machined notches [12] and volumetric elliptical voids [29]. The characterization of elliptical voids naturally extracts the aspect ratio, which is a measure of sharpness and could be useful in fatigue life predictions
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