Probabilistic ultrasound C-scan imaging of barely visible impact damage in CFRP laminates

Ultrasonic pulse-echo C-scan imaging is a widespread method for detecting and characterizing defects in fiber reinforced polymer composites. However, the accurate assessment of a complex distributed damage cluster, like barely visible impact damage, in multi-layer and heterogeneous composites is not straightforward. For reliably estimating the remaining load carrying capacity and/or remaining useful lifetime of a damaged composite, a proper and complete damage assessment is of utmost importance.

In this paper, a statistical time-energy gating approach is proposed in view of obtaining improved ultrasonic pulse-echo imaging of impacted composites. The majority of virgin A-scan signals are first clustered by analyzing their back-wall echoes. Next, using the principle of maximum likelihood, a Rice distribution is matched to the instantaneous amplitude in order to estimate the natural variability in the local energy of the virgin response signals. The resulting time-varying reliability interval provides an effective means to identify signals coming from defects or inhomogeneities, and as such to robustly assess defect parameters. The proposed probabilistic imaging procedure is demonstrated on various carbon fiber reinforced polymer laminates with barely visible impact damage. The obtained results are benchmarked by conventional ultrasonic C-scan imaging in through-transmission mode as well as in pulse-echo mode using the classical time gating approach. In contrast to the classical time gate method, the proposed statistical time-energy gating procedure successfully extracts and quantifies the full extent of the complex impact damage cluster. Further, the good noise resistance of the proposed probabilistic imaging method is demonstrated for a wide range of signal-to-noise ratios.

» Author: Jeroen Vandendriessche, Adil Han Orta, Erik Verboven, Wim Van Paepegem, Koen Van Den Abeele, Mathias Kersemans

» Reference: Composite Structures, Volume 284

» Publication Date: 15/03/2022

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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nº 768737


                   




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