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Weak bond detection in single-lap shear bonds by evaluating vibroacoustic modulations with artificial neural networks
Citation Link: https://doi.org/10.15480/882.5003
Publikationstyp
Conference Paper
Publikationsdatum
2022-06
Sprache
English
Volume
3
Start Page
509
End Page
516
Citation
20th European Conference on Composite Materials (ECCM 2022) 3: 509-516 (2022)
Contribution to Conference
Publisher DOI
Scopus ID
2-s2.0-85149172227
Publisher
Composite Construction Laboratory
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.
Schlagworte
Artificial Neural Networks
Composites
Nondestructive Testing
Vibroacoustic Modulation
Weak bonds
DDC Class
600: Technik