Ultrasonic imaging of damage in plates in spectral ripple frequency domain. (15th July 2023)

• Record Type: Journal Article
• Authors: Yang, Xiaoyu; Kersemans, Mathias
• Is Part Of: Mechanical systems and signal processing. Volume 195(2023)
• Publication Date: 2023-07-15
• Subjects: Composite -- Defect imaging -- Barely Visible Impact Damage -- Ultrasonic testing -- Spectral ripple frequency; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2023.110312
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Spectral ripple frequency analysis for interpreting ultrasound data. Efficient and automated defect imaging. Good noise resistance. High robustness against low sampling rate. Abstract: The ultrasonic C-scan method is a well-known non-destructive inspection method which typically analyzes ultrasound echoes in time domain. Although demonstrated for a variety of defects and materials, it has certain challenges in correctly assessing complex and distributed defects in multi-layer heterogeneous materials, e.g. barely visible impact damage in composite laminates. This paper proposes a C-scan imaging technique in the spectral ripple frequency domain by exploiting interference phenomena between multiple ultrasonic echoes in the response signal. The spectral ripple frequency domain imaging method processes the full A-scan signal without having to define an evaluation time gate. As such, it is highly suited for inspection of curved or tilted parts, or parts having different thickness sections. The method is demonstrated on several 5.5 mm thick carbon fiber reinforced polymer laminates with barely visible impact damage, revealing its capability to resolve neighboring delaminations and small defect fragments over the entire depth of the sample. The spectral ripple frequency domain imaging method shows good performance on datasets with high noise levels and/or low sampling rates, and is computationally very efficient, allowing real-time defect imaging.