Miniaturized multi-modality field-ready sensing system for defect detection of CFRP materials

Probst, Paul; Piao, Guanyu; Kumar, Deepak; Peng, Lei; Kotriwar, Yamini; Srinivasan, Vijay; Davis, Eric; Constable, John; Wong, J.; Deng, Yiming

doi:10.1016/j.ndteint.2023.102815

Cited by 8 publications

(2 citation statements)

References 33 publications

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“…In order to improve the SNR and expand the scanning range, multiple waveforms from longitudinal, transverse and guided waves can be adopted for imaging at the same time in DSAS [47,48]. Other methodologies [49] such as thermography and microwave imaging can be incorporated for a multi-modalities system in the future to improve the reliability. To solve the contradiction between the hardware and the need for real-time detection in DSAS, the computation speed can be improved through subarray average [30] and sparse aperture [50].…”

Section: Discussionmentioning

confidence: 99%

High-resolution defect imaging of composites using delay-sum-and-square beamforming algorithm

Zhao,

Zhang,

Zhang

et al. 2024

Meas. Sci. Technol.

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High-resolution ultrasonic imaging for defects in anisotropic multilayer carbon fiber reinforced polymers (CFRPs) is challenging because of the severe ultrasonic attenuation and the low signal-to-noise ratio (SNR) of echoes. The existing delay-multiply-and-sum (DMAS) beamforming outperforms delay-and-sum (DAS) beamforming in resolution, but with high computational complexity and energy loss. This paper presents a novel delay-sum-and-square (DSAS) beamforming algorithm. It takes full advantage of spatial coherence of captured data in the receiving and transmitting apertures. The non-coherent components caused by background noise are suppressed during the imaging. The back-wall reflection method (BRM) is used to correct the direction-dependent velocity. Full-matrix data is experimentally captured and processed on three different CFRP samples. Compared with DAS and DMAS, DSAS has a significant improvement in resolution, SNR and contrast. It demonstrates excellent defect characterization and noise suppression capability with only 17.4% computation time of DMAS.

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Section: Discussionmentioning

confidence: 99%

High-resolution defect imaging of composites using delay-sum-and-square beamforming algorithm

Zhao,

Zhang,

Zhang

et al. 2024

Meas. Sci. Technol.

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“…Various methods have been proposed for detecting material defects, such as optical microscopy, [ 8 ] ultrasonic testing (UT), [ 9 ] X‐ray computed tomography (XCT), [ 10,11 ] terahertz (THz) characterization, [ 12 ] capacitive and microwave imaging, [ 13 ] and the high‐frequency eddy‐current (HFEC) method. [ 14 ] Although optical microscopy can detect surface defects in materials, it cannot efficiently and accurately locate internal defects.…”

Section: Introductionmentioning

confidence: 99%

Self‐Reporting Microsensors Inspired by Noctiluca Scintillans for Small‐Defect Positioning and Electrical‐Stress Visualization in Polymers

Sima,

Yang,

Sun

et al. 2024

Advanced Materials

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Abstractsmall defects induce concentrated electrical stress in dielectric polymers, leading to premature failure of materials. Existing sensing methods fail to effectively visualize these defects owing to the invisible‐energy state of the electric field. Thus, it is necessary to establish a nondestructive method for the real‐time detection of small defects in dielectric polymers. In this study, a self‐reporting microsensor (SRM) inspired by Noctiluca scintillans is designed to endow materials with the ability of self‐detection for defects and electrical stress. The SRM leverages the energy of a nearby electric field to emit measurable fluorescence, enabling defect localization and diagnosis as well as electrical‐stress visualization. A controllable dielectric microsphere is constructed to achieve an adjustable electroluminescence threshold for the SRM, thereby increasing its detection accuracy while decreasing the electroluminescence threshold. The potential degradation in the polymer performance owing to SRM implantation is addressed by assembling long molecular chains on the SRM surface to spontaneously generate an interpenetrating network. Results of finite element analyses and experiments demonstrate that the SRM can effectively realize nondestructive visualization and positioning of small defects and concentrated electrical stress in polymers, positioning it as a promising sensing method for monitoring the electric field and charge distribution in materials.This article is protected by copyright. All rights reserved

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Multi-channel capacitive sensing system for cross bore detection and classification by machine learning

Piao

Desai

et al. 2023

NDT & E International

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Miniaturized multi-modality field-ready sensing system for defect detection of CFRP materials

Cited by 8 publications

References 33 publications

High-resolution defect imaging of composites using delay-sum-and-square beamforming algorithm

High-resolution defect imaging of composites using delay-sum-and-square beamforming algorithm

Self‐Reporting Microsensors Inspired by Noctiluca Scintillans for Small‐Defect Positioning and Electrical‐Stress Visualization in Polymers

Multi-channel capacitive sensing system for cross bore detection and classification by machine learning

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