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A Bayesian level set method for microtexture region characterization using eddy current data

Authors: Laura Homa (University of Dayton Research Institute) , Matthew Cherry (Air Force Research Laboratory) , Daniel Sparkman (Air Force Research Laboratory)

  • A Bayesian level set method for microtexture region characterization using eddy current data

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    A Bayesian level set method for microtexture region characterization using eddy current data

    Authors: , ,

Abstract

The presence of microtexture regions (MTRs) in engine components made of titanium alloys can have significant impact on the life of those components. While it has been established that eddy current methods are sensitive to MTRs, work has begun only recently to determine the ability of eddy current methods to characterize MTRs. In this work, we propose using Bayesian level set inversion to determine the size and shape of MTRs using eddy current data. The method is applied to a simple test problem: determining the size of an elliptical MTR using the simulated eddy current signal. Extensions to specimens with more realistic geometries are also discussed.

How to Cite:

Homa, L. ., Cherry, M. . & Sparkman, D. ., (2019) “A Bayesian level set method for microtexture region characterization using eddy current data”, Review of Progress in Quantitative Nondestructive Evaluation .

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Published on
03 Dec 2019
Peer Reviewed
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