Abstract
This work presents an innovative multi-physics (MP) Learning-by-Examples (LBE) inversion methodology for real-time non-destructive testing (NDT). Eddy Current Testing (ECT) and Ultrasonic Testing (UT) data are effectively combined to deal with the localization and characterization of a crack inside a conductive structure. An adaptive sampling strategy is applied on ECT-UT data in order to build an optimal (i.e., having minimum cardinality and highly informative) training set. Support vector regression (SVR) is exploited to obtain a computationally-efficient and accurate surrogate model of the inverse operator and, subsequently, to perform real-time inversions on previously-unseen measurements provided by simulations. The robustness of the proposed MP-LBE approach is numerically assessed in presence of synthetic noisy test set and compared to single-physic (i.e., ECT or UT) inversion.
Highlights
Real time accurate inversion solution becomes the main priority in non-destructive testing and evaluation (NDT-NDE) applications
Whereas ultrasound testing (UT) NDT inspection is suitable for high resolution, but the inspection is affected by the surface roughness of the inspected medium [9]
I is the current flowing inside the coil while Einc(r|rk) is the incident field generated at position r in the unflawed plate (rk = represents the k-th coil position within the plate). ρ(r|rk) is the unknown induced current dipole density, which models the presence of the crack and is related to the total field, Etot(r|rk) that can be expresses by ρ(r|rk) = [σ(r) − σ]Etot(r|rk)
Summary
Real time accurate inversion solution becomes the main priority in non-destructive testing and evaluation (NDT-NDE) applications. LBE has been adopted for a NDE problem where a narrow crack is occurred around a fastener (e.g., bore hole) within an inspected medium [8]. This is an important problem for the aging aircraft NDE community and Eddy Current Testing (ECT) is widely applied while the structure under test (SUT) thickness is thin. Multi-physics (MP) data fusion (ECT-UT) has been applied to maximize the inversion performance for crack characterization and localization. The performance of the MP-LBE inversion schema for crack characterization and localization is compared to single-physic (i.e., ECT and UT) inversion on noisy data.
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