Assessing performance of flaw characterization methods through uncertainty propagation

Miorelli, Roberto; Bourdais, Florian Le; Artusi, Xavier

doi:10.1063/1.5031625

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…A type of more general inversion procedures iteratively update the defect geometry according to the difference between the actual and predicted measurements until a satisfactory agreement is achieved [23], [24]. Metamodels (or surrogate models) can speed up the inversion process by enabling fast evaluation of the forward model [25] and are recently used as tools for propagation of uncertainties [26]. Machine learning (ML) algorithms [27] offer another possibility to address the inverse problem, in which measurements from a number of known defects are used to train a suitable classifier [28] or regressor [29], [30].…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Defect Characterization Using the Scattering Matrix: A Performance Comparison Study of Bayesian Inversion and Machine Learning Schemas

Bai

Bourdais

Miorelli

et al. 2021

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Accurate defect characterisation is desirable in ultrasonic non-destructive evaluation as it can provide quantitative information about the defect type and geometry. For defect characterisation using ultrasonic arrays, high resolution images can provide the size and type information if a defect is relatively large. However, the performance of image-based characterisation becomes poor for small defects that are comparable to the wavelength. An alternative approach is to extract the far-field scattering coefficient matrix from the array data and use it for characterisation. Defect characterisation can be performed based on a scattering matrix database that consists of the scattering matrices of idealised defects with varying parameters. In this paper, the problem of characterising small surface-breaking notches is studied using two different approaches. The first approach is based on the introduction of a general coherent noise model, and it performs characterisation within the Bayesian framework. The second approach relies on a supervised machine learning (ML) schema based on a scattering matrix database, which is used as the training set to fit the ML model exploited for the characterisation task. It is shown that convolutional neural networks (CNNs) can achieve the best characterisation accuracy among the considered machine learning approaches and they give similar characterisation uncertainty to that of the Bayesian approach if a notch is favourably oriented. The performance of both approaches varied for unfavourably oriented notches, and the machine learning approach tends to give results with higher variance and lower biases.

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Section: Introductionmentioning

confidence: 99%

Ultrasonic Defect Characterization Using the Scattering Matrix: A Performance Comparison Study of Bayesian Inversion and Machine Learning Schemas

Bai

Bourdais

Miorelli

et al. 2021

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Global Sensitivity Analysis of Ultrasonic Testing Simulations of Slot-Like Defects With Multifidelity Modeling

Liu,

Miorelli,

Leifsson

et al. 2024

Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems

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In this paper, an efficient global sensitivity analysis (GSA) method for simulation-based ultrasonic testing (UT) of slot-like defects using multifidelity modeling with novel termination criterion is proposed. GSA quantifies the effect of quantities of interest with variability (e.g., position, height, and angle) on the output (e.g., amplitude). GSA with Sobol' indices requires the use of Monte Carlo simulations (MCS) when dealing with nonlinear problems having many parameters. It is impractical to perform GSA directly on high-fidelity physics-based models due to their long evaluation times and the large number of required samples. Multifidelity methods construct surrogate models based on data from an accurate high-fidelity model (HFM) and fast low-fidelity models (LFMs). The multifidelity surrogates evaluate quickly and can be used in lieu of the HFM to accelerate the GSA. Conventional multifidelity methods construct the surrogate to meet a prespecified error metric before using it within an analysis. This requires a separate set of testing data and an often arbitrary error metric threshold. To avoid these, a novel multifidelity modeling termination criterion for GSA is proposed that is based on the absolute relative change of the Sobol' indices. The proposed approach is demonstrated on a simulated UT case inspecting a slot-like defect with three uncertainty variables. The results show a potential for significant reduction in computational cost compared with conventional approaches.

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Assessing performance of flaw characterization methods through uncertainty propagation

Cited by 2 publications

References 9 publications

Ultrasonic Defect Characterization Using the Scattering Matrix: A Performance Comparison Study of Bayesian Inversion and Machine Learning Schemas

Ultrasonic Defect Characterization Using the Scattering Matrix: A Performance Comparison Study of Bayesian Inversion and Machine Learning Schemas

Global Sensitivity Analysis of Ultrasonic Testing Simulations of Slot-Like Defects With Multifidelity Modeling

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