Simulation of ultrasonic TFM/FMC imaging for porosity clusters using multiple scattering modelling: Quantitative analyses and experimental comparisons

Vasseur, Julien; Leymarie, Nicolas; Dorval, Vincent; Dupont, Benoît; Vasiukov, Dmytro; Chaki, S.

doi:10.1016/j.ndteint.2023.102827

Cited by 5 publications

(2 citation statements)

References 27 publications

(31 reference statements)

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“…Compared to other data acquisition methods, FMC data contains more information and demonstrates potential for solving various issues. In contrast to conventional ultrasonic phased array detection, the total focusing method (TFM), based on FMC, can achieve focalization at any selected point within the measurement area by post-processing the full matrix data acquired from the transducer array [11,12].…”

Section: Introductionmentioning

confidence: 99%

Multi-layered medium ultrasonic phased array sparse TFM imaging based on self-adaptive differential evolution algorithm

Yao,

Zhao,

et al. 2024

Meas. Sci. Technol.

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Multilayer Composite material structures have been widely used in modern engineering fields. However, defects within these materials can adversely affect mechanical properties. Ultrasonic phased array Total Focusing Method (TFM) imaging has advantages of high precision and dynamic focusing over the entire range, achieving significant progress in homogeneous medium detection. However, heavy computational burdens of multilayer structures lead to inefficient imaging. To address this issue, a sparse-TFM imaging algorithm using ultrasonic phased arrays suitable for multilayer media is proposed in this paper. This method constructs a fitness function with constraints such as main lobe width and sidelobe peak. Its objective is to obtain the distribution of sparse array element positions using an Self-adaptive differential evolution algorithm(SaDE). Subsequently, the delay time of each array element in multilayer media sparse TFM is calculated using the Root Mean Square (RMS) principle and combined with amplitude weighting, the method corrects the imaging results. Compared with the Ray-based full-matrix capture and total focusing method method (Ray-based FMC/TFM), the root mean square-based full-matrix capture and total focusing method (RMS-based FMC/TFM), and the phase shift method (PSM), the experimental and simulation results demonstrate that the proposed method significantly reduces the imaging data volume, improves computational efficiency, and maintains quantitative errors within 0.2 mm.

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Section: Introductionmentioning

confidence: 99%

Multi-layered medium ultrasonic phased array sparse TFM imaging based on self-adaptive differential evolution algorithm

Yao,

Zhao,

et al. 2024

Meas. Sci. Technol.

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show abstract

“…Quantitative analysis of ultrasonic signals can provide information about the material properties of a component, such as its elastic modulus, density, etc. This information can be used to determine the suitability of the material for its intended application [14]. In [15], NDE using the quantification of subsurface defects captured by ultrasound is explained, the authors use principal component analysis for dimensionality reduction of the data and supervised learning algorithms for the detection of the defects.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Manufacturing Parameters of Additively Manufactured 316l Steel Samples Using Ultrasound Fingerprinting

Zia¹,

Carlson²,

Åkerfeldt³

2023

Preprint

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Prediction of manufacturing parameters of additively manufactured 316L steel samples using ultrasound fingerprinting

Zia,

Carlson,

Åkerfeldt

2024

Ultrasonics

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Simulation of ultrasonic TFM/FMC imaging for porosity clusters using multiple scattering modelling: Quantitative analyses and experimental comparisons

Cited by 5 publications

References 27 publications

Multi-layered medium ultrasonic phased array sparse TFM imaging based on self-adaptive differential evolution algorithm

Multi-layered medium ultrasonic phased array sparse TFM imaging based on self-adaptive differential evolution algorithm

Prediction of Manufacturing Parameters of Additively Manufactured 316l Steel Samples Using Ultrasound Fingerprinting

Prediction of manufacturing parameters of additively manufactured 316L steel samples using ultrasound fingerprinting

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