Microtexture Region Segmentation Using Matching Component Analysis Applied to Eddy Current Testing Data

Homa, Laura; Lorenzo, Nick; Cherry, Matthew; Wertz, John

doi:10.1007/s10921-023-00951-z

Cited by 2 publications

(7 citation statements)

References 16 publications

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“…However, as was shown in [33], this solution will not behave as a segmentation should, i.e. it will not be spatially piecewise constant.…”

Section: Segmentation Using Matching Component Analysismentioning

confidence: 93%

“…The basic procedure for segmentation using CGMCA was developed in [33]. The CGMCA maps g 1 and g 2 are trained on a set of known microstructures with known MTR segmentation matched to the corresponding ECT data of each microstructure, as shown in figure 4.…”

Section: Segmentation Using Matching Component Analysismentioning

confidence: 99%

“…In [33], the piecewise constant requirement was enforced by utilizing level set inversion to find s * , while in [34], a penalty term was added to the inversion. Specifically, in [34], a regularization method developed for image deblurring in [35] was applied, which we review briefly in the next subsection.…”

Section: Segmentation Using Matching Component Analysismentioning

confidence: 99%

“…We chose the matrices Γ 1 and Γ 2 for each CGMCA map in the same manner as in [33]. Specifically, we assume that Γ 1 = Γ 2 and assign a single covariance matrix Γ ∈ R k×k to the common domain.…”

Section: Training Of Cgmca Mapsmentioning

confidence: 99%

“…where p ℓ is the ℓth column of P 1 and p is the mean vector of the columns of P 1 . This choice of Γ was shown to work well for in the segmentation algorithm in [33]. The dimension of the common domain k is chosen based on the singular values of S 1 , in a similar manner as in [33].…”

Section: Training Of Cgmca Mapsmentioning

confidence: 99%

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Microtexture region segmentation of eddy current testing data using a structural prior

Homa,

Lesthaeghe,

Cherry

et al. 2024

Inverse Problems

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Microtexture regions (MTR) are collections of grains with similar crystallographic orientation. Because their presence in titanium alloys can significantly impact aerospace component life, a nondestructive method to detect and characterize MTR is needed. In this work, we propose to use data from two nondestructive evaluation methods, eddy current testing (ECT) and scanning acoustic microscopy (SAM), in order to recover the boundary and dominant crystallographic orientation of each MTR in a specimen. Eddy current testing is an electromagnetic method that is sensitive to changes in crystallographic orientation associated with MTR; however, its low resolution prevents it from resolving MTR boundaries well. In contrast, scanning acoustic microscopy is a high frequency ultrasound method that is able to resolve MTR boundaries but is not sensitive to orientation. This paper proposes an algorithm to characterize MTR that makes use of a method known as covariance generalized matching component analysis. This method is used to build a surrogate linear forward model that relates MTR boundaries and orientation to ECT data. The model is inverted using the SAM data as a structural prior. We demonstrate this technique using simulated ECT and experimental SAM data from a large grain titanium specimen.

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“…However, as was shown in [33], this solution will not behave as a segmentation should, i.e. it will not be spatially piecewise constant.…”

Section: Segmentation Using Matching Component Analysismentioning

confidence: 93%

Section: Segmentation Using Matching Component Analysismentioning

confidence: 99%

Section: Segmentation Using Matching Component Analysismentioning

confidence: 99%

Section: Training Of Cgmca Mapsmentioning

confidence: 99%

Section: Training Of Cgmca Mapsmentioning

confidence: 99%

See 3 more Smart Citations

Microtexture region segmentation of eddy current testing data using a structural prior

Homa,

Lesthaeghe,

Cherry

et al. 2024

Inverse Problems

Self Cite

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Nondestructive Testing and Health Monitoring Techniques for Structural Effective Prestress

Jia,

Zhang,

et al. 2023

Structural Control and Health Monitoring

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Prestressed structures are widely employed in bridges and large-span spatial structures, and the accurate evaluation of prestress state is of great importance for structural maintenance. This paper reviews the nondestructive testing (NDT) and health monitoring techniques for structural effective prestress. Specifically, the fiber Bragg grating (FBG) sensor-based, magnetic-elastic (ME) sensor-based, dynamic response-based, ultrasonic guided wave (UGW)-based, electromechanical impedance (EMI)-based, and electrical resistance-based methods are reviewed in this paper. Firstly, the principle, application range, and measuring accuracy of each technique are introduced and analyzed, and the benefits and limitations of each technique are summarized: The FBG sensor and ME sensor take on high measuring accuracy and have been applied in practical engineering, but they are required to be preinstalled during structural construction; the dynamic response-based method is greatly effective in cable force assessment but not suitable for prestress evaluation of prestressed concrete (PSC) structures; the UGW-based, EMI-based, and electrical resistance-based methods have shown favorable potential for prestress assessment in laboratory experiments, but their feasibility and accuracy in practical engineering need to be verified. Secondly, the challenges and discussion of each method are discussed in the following four aspects: measuring range, reliability of measuring results, stability and durability considering long-term monitoring, and cost-efficiency. Finally, a decision tree is proposed to choose the most appropriate prestress evaluation method in a specific application scenario.

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Microtexture Region Segmentation Using Matching Component Analysis Applied to Eddy Current Testing Data

Cited by 2 publications

References 16 publications

Microtexture region segmentation of eddy current testing data using a structural prior

Microtexture region segmentation of eddy current testing data using a structural prior

Nondestructive Testing and Health Monitoring Techniques for Structural Effective Prestress

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