Influence of the uncertainty of elastic constants on the modelling of ultrasound propagation through multi-pass austenitic welds. Impact on non-destructive testing

Gueudré, Cécile; Mailhé, Jean; Ploix, Marie-Aude; Corneloup, Gilles; Chassignole, Bertrand

doi:10.1016/j.ijpvp.2019.02.011

Cited by 11 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regularization involves adding terms to the cost function equation (3.3), which are then included in the gradient calculation at each step. In this work, we used generally available regularization schemes, following intuitions from observations of metallography/EBSD examinations of welds [2,6,9,17,[28][29][30]. They show that the variation of angles is typically smooth, with just a few abrupt changes.…”

Section: (Ii) Transducer Configurationmentioning

confidence: 99%

Weld map tomography for determining local grain orientations from ultrasound

Kalkowski,

Lowe,

Samaitis

et al. 2023

Proc. R. Soc. A.

View full text Add to dashboard Cite

The complex structure of inhomogeneous welds poses a long-standing challenge in ultrasonic non-destructive testing. Elongated grains with spatially varying dominant orientations can distort and split the ultrasonic beam, hindering inspection data interpretation. One way to tackle this problem is to include material information in imaging and signal analysis; however, such information is often only gathered using destructive methods. This paper reports the development of a physics-based weld inversion strategy determining grain orientations using a ray tomography principle. The considered approach does not rely on a macroscopic weld description but may incorporate it to facilitate inversion. Hence, it is more general than other available approaches. The methodology is demonstrated in both numerical and experimental examples. The experimental work focuses on mock-up samples from the nuclear industry and a sample manufactured during this research. The ‘ground truth’ for the latter comes from an EBSD evaluation—the most accurate (yet destructive) examination technique available. Across the considered specimens, our methodology yielded orientation maps with average errors well below 20 ∘ , leading to time-of-flight errors below 0.05 μ s . Applying the result from inversion to ultrasonic imaging offered between 5 and 14 dB signal-to-noise ratio improvement for defect signatures.

show abstract

Section: (Ii) Transducer Configurationmentioning

confidence: 99%

Weld map tomography for determining local grain orientations from ultrasound

Kalkowski,

Lowe,

Samaitis

et al. 2023

Proc. R. Soc. A.

View full text Add to dashboard Cite

show abstract

“…The resulting relationship between the incident wave direction and longitudinal group velocity is plotted in Figure 1b. Note that in this paper we assume a priori knowledge of the elastic stiffness constants, however in practice these would need to be approximated experimentally and the uncertainty associated with these approximations considered [33,34]. There are several models that describe how microstructural properties vary throughout a weld depending on various welding parameters [13,29,33,35].…”

Section: Weld Modelsmentioning

confidence: 99%

Deep Learning Based Inversion of Locally Anisotropic Weld Properties from Ultrasonic Array Data

et al. 2022

View full text Add to dashboard Cite

The ability to reliably detect and characterise defects embedded in austenitic steel welds depends on prior knowledge of microstructural descriptors, such as the orientations of the weld’s locally anisotropic grain structure. These orientations are usually unknown but it has been shown recently that they can be estimated from ultrasonic scattered wave data. However, conventional algorithms used for solving this inverse problem incur a significant computational cost. In this paper, we propose a framework which uses deep neural networks (DNNs) to reconstruct crystallographic orientations in a welded material from ultrasonic travel time data, in real-time. Acquiring the large amount of training data required for DNNs experimentally is practically infeasible for this problem, therefore a model based training approach is investigated instead, where a simple and efficient analytical method for modelling ultrasonic wave travel times through given weld geometries is implemented. The proposed method is validated by testing the trained networks on data arising from sophisticated finite element simulations of wave propagation through weld microstructures. The trained deep neural network predicts grain orientations to within 3° and in near real-time (0.04 s), presenting a significant step towards realising real-time, accurate characterisation of weld microstructures from ultrasonic non-destructive measurements. The subsequent improvement in defect imaging is then demonstrated via use of the DNN predicted crystallographic orientations to correct the delay laws on which the total focusing method imaging algorithm is based. An improvement of up to 5.3 dB in the signal-to-noise ratio is achieved.

show abstract

“…Figure 2 shows the metal structure of the weld. The size and direction of crystal grains are determined according to various parameters, such as the welding temperature and cooling rate [ 27 , 28 ]. Although the grain orientation slightly differs during solidification depending on various conditions, the grains usually grow vertically on the groove surface, and their orientation slightly changes toward the center.…”

Section: Austenitic Steel Weld and Pa Probe Modelingmentioning

confidence: 99%

Analysis of Flaw Detection Sensitivity of Phased Array Ultrasonics in Austenitic Steel Welds According to Inspection Conditions

Kim

Cho

Park

2021

Sensors

View full text Add to dashboard Cite

The anisotropy and inhomogeneity exhibited by austenitic steel in welds poses a challenge to nondestructive testing employing ultrasonic waves, which is predominantly utilized for the inspection of welds in power plants. In this study, we assess the reliability of phased array ultrasonic testing (PAUT) by analyzing the flaw detection sensitivity of ultrasonic beams in anisotropic welds, based on the inspection conditions. First, we simulated the sectorial scan technique, frequently employed for the inspection of actual welds, while taking into account the ultrasonic wave mode, frequency, and shape and position of a flaw. Subsequently, we analyzed the flaw sensitivity by comparing A-scan signals and S-scan results. The sensitivity analysis results confirmed the detection of all flaws by considering at least two inspection methods based on the shape and position of the flaw. Furthermore, we verified our model by performing an experiment under the same conditions as the simulation and found that the results were in agreement. Hence, we find that the simulation modeling technique proposed in this study can be utilized to develop suitable inspection conditions, according to the flaw characteristics or inspection environment.

show abstract

Influence of the uncertainty of elastic constants on the modelling of ultrasound propagation through multi-pass austenitic welds. Impact on non-destructive testing

Cited by 11 publications

References 24 publications

Weld map tomography for determining local grain orientations from ultrasound

Weld map tomography for determining local grain orientations from ultrasound

Deep Learning Based Inversion of Locally Anisotropic Weld Properties from Ultrasonic Array Data

Analysis of Flaw Detection Sensitivity of Phased Array Ultrasonics in Austenitic Steel Welds According to Inspection Conditions

Contact Info

Product

Resources

About