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Detection and Separation of Close Flaws in Coarse-Grained Materials Using Ultrasonic Image Deconvolution

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Abstract

Ultrasonic inspection of coarse-grained steels is a common challenge in various industrial fields. This task is often difficult because of acoustic scattering that creates structural noise in the ultrasonic signals and images. Therefore, inspections usually use low-frequency probes, which achieve poor resolution with standard delay-and-sum (DAS) imaging techniques, such as the well-known total focusing method (TFM). The purpose of this paper is to evaluate the performance of a super-resolution ultrasonic imaging technique presented by Laroche et al. (IEEE Trans Comput Imaging 7:935–947, 2021) for the inspection of industrial coarse-grained materials. An image deconvolution problem (with spatially varying blur) is formulated, relying on a forward model that links the TFM image to the acoustic reflectivity map. The experiments consider an austenitic-ferritic stainless steel sample insonified using array probes at 3 MHz and 5 MHz placed in contact. The goal is to resolve two close reflectors corresponding to side-drilled holes (SDH) with diameter 0.4 mm spaced by 0.4 mm edge-to-edge and positioned at different depths (10, 20, 30, 40 mm). This configuration corresponds to a critical case where the distance between the two reflectors is much lower than the Rayleigh distance, that is the resolution limit of a DAS imaging system. These are typical cases where DAS images obtained from low-frequency inspections critically lack resolution, but where higher frequency probes cannot be used in practice, because a too week signal-to-noise ratio would affect the detection capability. As predicted by the Rayleigh criterion, TFM is not able to separate the reflectors. The proposed image reconstruction method successfully resolves the majority of the reflectors with a rather accurate distance estimation. In the context of coarse-grained structure inspection, subwavelength reflectors distant from each other by two times less than the resolution limit given by the Rayleigh criterion have been successfully detected and separated. This approach hence enables the use of low-frequency probes, in order to improve the signal-to-noise ratio, while keeping high resolution capability which can be particularly interesting for industrial applications. In particular, the proposed approach shows promising results for the sizing of a real crack in an industrial sample.

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Funding

This work was partially funded by the French ANRT (Association Nationale Recherche Technologie), Project 2017/ 1083.

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Authors and Affiliations

  1. The Phased Array Company/DB-SAS, 13 rue du Bois Briand, Impasse de la Turquoise, 44300, Nantes, France

    Nans Laroche & Ewen Carcreff

  2. LS2N, 1 rue de la Noë, 44321, Nantes, France

    Nans Laroche, Sébastien Bourguignon & Jérôme Idier

  3. LAUM, avenue Olivier Messiaen, 72085, Le Mans, France

    Aroune Duclos

  4. EDF R &D, Site des renardières, avenue des renardières, 77250, Ecuelles, France

    Pierre-Emile Lhuillier

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  1. Nans Laroche
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  2. Ewen Carcreff
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  4. Jérôme Idier
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  5. Aroune Duclos
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Correspondence to Nans Laroche.

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Laroche, N., Carcreff, E., Bourguignon, S. et al. Detection and Separation of Close Flaws in Coarse-Grained Materials Using Ultrasonic Image Deconvolution. J Nondestruct Eval 41, 66 (2022). https://doi.org/10.1007/s10921-022-00900-2

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