Estimation of microtexture region orientation distribution functions using eddy current data

Abstract

Microtexture regions (MTRs) within titanium alloys are collections of grains with similar crystallographic orientation. The presence of MTRs can be detrimental to the life of an engine component; as such, a method to detect and characterize MTRs is needed. One potential technique to characterize MTRs is eddy current (EC) testing, which is a nondestructive evaluation technique that is sensitive to the changes in local conductivity due to changes in local crystallographic orientation. While it has been established in the literature that EC methods are sensitive to the presence of MTRs, work has begun only recently to determine the ability of EC to characterize MTRs. In this work, we develop an algorithm to estimate the orientation distribution function of an MTR of interest using EC data. The method relies on an application of the central limit theorem, and the inverse problem is solved in using a likelihood-free technique. Numerical examples using simulated EC data are shown.

Extended datasets for multi-scale mixed modality microstructure assessment of titanium

“…Examples include the use of wavelet decomposition to fuse ultrasound C-scans with optical images of barely visible impact damage in polymer-matrix composites as demonstrated by Katunin et al; or, the use of supervised and unsupervised learning techniques in fusing NDE of concrete structures as demonstrated by Cotič et al 14,15 Researchers like Dion et al and Kahrobaee have also used multimodal data fusion to improve bulk characterization of microstructure properties, such as determining the processing conditions in as-manufactured parts or the volume percent of ferrite in dual phase steels 16,17 . Yet, only recently has fusion of NDE modalities for materials characterization at the microscale been demonstrated [18][19][20][21][22] .…”

Exploring high-frequency eddy-current testing for sub-aperture defect characterisation using parametric-manifold mapping

“…Conversely, Cherry et.al. [13] have developed an approximate impedance integral model to characterise grain microtexture in Titanium superalloys [14] and use it to improve sub-mm defect characterisation in these materials [15]. Others have developed meta-models of the defect database and optimisation techniques to improve the speed of characterisation [16,17,18], while approaches employing artificial intelligence and machine learning methods have also been explore with some success [9,19,20,21,22].…”