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Non-contact Lamb wave defect detection based solely on air-coupled ultrasonic phased arrays

Laub, Felix ; Haugwitz, Christoph ; Allevato, Gianni ; Seiler, Julian ; Findeisen, Rolf ; Kupnik, Mario (2023)
Non-contact Lamb wave defect detection based solely on air-coupled ultrasonic phased arrays.
2023 IEEE SENSORS. Vienna, Austria (29.10. - 01.11.2023)
doi: 10.1109/SENSORS56945.2023.10324898
Conference or Workshop Item, Bibliographie

Abstract

Air-coupled ultrasonic non-destructive testing (NDT) enables the inspection of composite materials for defects. In this paper, we investigate non-contact defect detection based on Lamb waves by using two dedicated air-coupled ultrasonic phased arrays, i.e. one for transmission and one for reception. The major challenge is the signal detection of the reflected leaky Lamb wave originating from the defect, despite a strong interfering signal, i.e. the direct air-path wave, causing a so-called blind zone. Therefore, we exploit the angular difference of the reflected leaky Lamb wave and the direct air-path wave by applying high-resolution direction of arrival (DoA) estimation algorithms, i.e. MVDR beamformer (Capon) and MUSIC algorithms, as well as image deconvolution methods, i.e. CLEAN and Richardson-Lucy algorithms. First, we assess the signal detection capability by using Monte Carlo simulations considering realistic non-ideal array elements. Second, we validate the simulations by conducting testbed measurements, which show that conventional beamforming (CBF), MVDR and MUSIC are capable of fully air-coupled defect detection, even when the direct air-path wave is impinging. We conclude that the blind zone can be eliminated and show that the MUSIC algorithm excels in highlighting the reflected leaky Lamb wave.

Item Type: Conference or Workshop Item
Erschienen: 2023
Creators: Laub, Felix ; Haugwitz, Christoph ; Allevato, Gianni ; Seiler, Julian ; Findeisen, Rolf ; Kupnik, Mario
Type of entry: Bibliographie
Title: Non-contact Lamb wave defect detection based solely on air-coupled ultrasonic phased arrays
Language: English
Date: 28 November 2023
Place of Publication: Piscataway
Publisher: IEEE
Book Title: IEEE SENSORS 2023: Conference Proceedings
Event Title: 2023 IEEE SENSORS
Event Location: Vienna, Austria
Event Dates: 29.10. - 01.11.2023
DOI: 10.1109/SENSORS56945.2023.10324898
Abstract:

Air-coupled ultrasonic non-destructive testing (NDT) enables the inspection of composite materials for defects. In this paper, we investigate non-contact defect detection based on Lamb waves by using two dedicated air-coupled ultrasonic phased arrays, i.e. one for transmission and one for reception. The major challenge is the signal detection of the reflected leaky Lamb wave originating from the defect, despite a strong interfering signal, i.e. the direct air-path wave, causing a so-called blind zone. Therefore, we exploit the angular difference of the reflected leaky Lamb wave and the direct air-path wave by applying high-resolution direction of arrival (DoA) estimation algorithms, i.e. MVDR beamformer (Capon) and MUSIC algorithms, as well as image deconvolution methods, i.e. CLEAN and Richardson-Lucy algorithms. First, we assess the signal detection capability by using Monte Carlo simulations considering realistic non-ideal array elements. Second, we validate the simulations by conducting testbed measurements, which show that conventional beamforming (CBF), MVDR and MUSIC are capable of fully air-coupled defect detection, even when the direct air-path wave is impinging. We conclude that the blind zone can be eliminated and show that the MUSIC algorithm excels in highlighting the reflected leaky Lamb wave.

Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik
18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control and Cyber-Physical Systems (CCPS)
Date Deposited: 13 Mar 2024 10:42
Last Modified: 21 Jun 2024 12:21
PPN: 519326083
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