Model-based inverse methods for sizing surface-breaking discontinuities with eddy current probe variability

The objective of this work is to demonstrate and validate model-based inversion techniques to characterize length, depth, width and orientation of surface-breaking cracks using eddy current NDE under varying probe conditions. A series of parametric studies of probe characteristics are presented for...

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Hauptverfasser: Aldrin, John C., Shell, Eric B., Oneida, Erin K., Sabbagh, Harold A., Sabbagh, Elias, Murphy, R. Kim, Mazdiyasni, Siamack, Lindgren, Eric A.
Format: Tagungsbericht
Sprache:eng
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Zusammenfassung:The objective of this work is to demonstrate and validate model-based inversion techniques to characterize length, depth, width and orientation of surface-breaking cracks using eddy current NDE under varying probe conditions. A series of parametric studies of probe characteristics are presented for a fixed set of well-characterized flaws with varying length, depth, opening width and orientation angle. Results show inversion performance differences between probes with the same design specifications. Inversion results were also evaluated for a probe that was selectively controlled for varying probe liftoff, varying tilt in two directions, and orientation. Certain levels of probe tilt and liftoff were found to degrade the performance of the inversion technique. By using a model calibration process that incorporates the matching probe calibration data, better inversion results can be achieved, to a limited degree. There is a need to more appropriately adapt the model through the calibration fit to compensate for varying probe tilt and liftoff. Results are presented for a model transform approach, evaluating scale and phase terms based on the best model fit with the calibration data. The results for certain severe cases of liftoff were improved using the transformed model; however, it does not address all probe conditions. Future work is proposed to use a full model-based transformation approach using more comprehensive meta-model representations.
ISSN:0094-243X
1551-7616
DOI:10.1063/1.4940539