Estimation of Realistic Microtexture Region Orientation Distribution Functions Using Eddy Current Data

Homa, Laura; Cherry, Matthew; Wertz, John

doi:10.1007/s11837-022-05360-7

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…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] .…”

Section: The Role Of Data Fusionmentioning

confidence: 99%

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

Wertz,

Cherry,

Homa

et al. 2023

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII

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The sensors used for nondestructive evaluation (NDE) play a crucial role in ensuring aircraft availability. The current NDE paradigm often relies on mono-modal testing and signal-over-threshold criteria to provide robust defect or damage detection, not characterization. This approach works for go/no-go inspections of critical flaws that respond strongly to specific physical stimuli; for example, the electromagnetic method of eddy current testing (ECT) is sensitive enough to the abrupt change in conductivity of surface-breaking cracks in metals that it can be used exclusively for certain practical safety inspections. Yet, there are cases where this approach proves insufficient. Consider characterization of problematic microtexture regions (MTR) in certain titanium alloys, which exceeds the capabilities of any one NDE technique. In this work, a data fusion-based solution to MTR characterization is explored. The material problem and potential inspection methods are discussed. Registered datasets from these methods are presented and made available to the community.

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