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Title: Weak bond detection in single-lap shear bonds by evaluating vibroacoustic modulations with artificial neural networks
Language: English
Authors: Boll, Benjamin  
Willmann, Erik  
Fiedler, Bodo  
Meißner, Robert  
Keywords: Artificial Neural Networks; Composites; Nondestructive Testing; Vibroacoustic Modulation; Weak bonds
Issue Date: Jun-2022
Publisher: Composite Construction Laboratory
Source: 20th European Conference on Composite Materials (ECCM 2022) 3: 509-516 (2022)
Abstract (english): 
Adhesive bonding is an essential method for joining composite materials. However, the occurrence of contaminations, resulting in a not detectable weakened adhesion, persists. This study aims to uncover weak bonds with the vibroacoustic modulation method, a nonlinear ultrasonic testing method, where ultrasonic guided waves are modulated by a simultaneously applied, high amplitude pump wave. Afterwards, the measurements are evaluated by a deep learning approach. A previous dataset of 40 single-lap shear specimens (ASTM D5868-01), in which artificial interfaces in the form of circular PTFE films or release agent contaminations were introduced, was extended by a second dataset with 14 specimens of a different laminate to evaluate the robustness and transferability of the method. The proposed neural network approach can reliably recognize the bonding flaws in the training dataset and even has high accuracies on the transfer dataset, demonstrating the tremendous potential for the nondestructive evaluation of adhesive joints.
Conference: 20th European Conference on Composite Materials, ECCM 2022 
DOI: 10.15480/882.5003
ISBN: 978-2-9701-6140-0
Institute: Kunststoffe und Verbundwerkstoffe M-11 
Molekulardynamische Simulation weicher Materie M-EXK2 
Document Type: Chapter/Article (Proceedings)
License: CC BY-NC 4.0 (Attribution-NonCommercial) CC BY-NC 4.0 (Attribution-NonCommercial)
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