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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Meißner, Robert
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (8/8 displayed)
- 2023Searching the chemical space for effective magnesium dissolution modulators: a deep learning approach using sparse features
- 2022Adsorption of oleic acid on magnetite facets
- 2021Weak adhesion detection – enhancing the analysis of vibroacoustic modulation by machine learningcitations
- 2021Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models
- 2020A first-principles analysis of the charge transfer in magnesium corrosioncitations
- 2020ATR-FTIR in Kretschmann configuration integrated with electrochemical cell as in situ interfacial sensitive tool to study corrosion inhibitors for magnesium substrates
- 2019Data science based mg corrosion engineeringcitations
- 2019Data science based mg corrosion engineering
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document
Weak adhesion detection – enhancing the analysis of vibroacoustic modulation by machine learning
Abstract
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.