Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3655
Publisher DOI: 10.1016/j.compstruct.2021.114233
Title: Weak adhesion detection – enhancing the analysis of vibroacoustic modulation by machine learning
Language: English
Authors: Boll, Benjamin  
Willmann, Erik 
Fiedler, Bodo  
Meißner, Robert  
Keywords: Artificial neural networks;Composites;Non-destructive testing;Vibroacoustic modulation;Weak-bonds
Issue Date: 8-Jun-2021
Publisher: Elsevier
Source: Composite Structures 273: 114233 (2021-10-01)
Journal: Composite structures 
Abstract (english): 
Adhesive bonding is a well-established technique for composite materials. Despite advanced surface treatments and preparations, surface contamination and application errors still occur, resulting in localised areas with a reduced adhesion. The dramatic reduction of the bond strength limits the applicability of adhesive bonds and hampers further industrial adaptation. This study aims to detect weak-bonds due to manufacturing errors or contamination by analysing and interpreting the vibroacoustic modulation signals with the aid of machine learning. An ultrasonic signal is introduced into the specimen by a piezoceramic actuator and modulated through a low frequency vibration excited by a servo-hydraulic testing system. Tested samples are single-lap shear specimens, according to ASTM D5868-01, with artificial circular debonding areas introduced as PTFE-films or a release agent contamination. It is shown that an artificial neural network can identify various defects in the bonded joint robustly and is able to predict residual strengths and hence demonstrates great potential for non-destructive testing of adhesive joints.
URI: http://hdl.handle.net/11420/9881
DOI: 10.15480/882.3655
ISSN: 0263-8223
Institute: Kunststoffe und Verbundwerkstoffe M-11 
Document Type: Article
More Funding information: All authors thanks the Hamburg University of Technology for funding the I3‐Lab VAM.
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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