journal article·2023-02

How adhesives flow during joining

Marvin Kaufmann, Florian Flaig, Michael Müller, Holger Fricke, Till Vallée

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Abstract

The highly automated world of manufacturing industry offers new possibilities for the application of adhesives which are not fully utilized yet. In addition to the classical static stress and strength related topics, a more global view on adhesive bonding is needed. This includes the consideration of adhesive flow during application and joining of the substrates with all the information about initial application pattern and the resulting adhesive distribution contained after joining, and thus benefit from the modern manufacturing possibilities. To do so, research which addresses this issue is needed in the first place to elaborate innovative approaches on how to design and develop adhesive application pattern which minimize adhesive spillage and waste while achieving a bond without any air enclosures or other imperfections. This paper systematically investigates different phenomena which can occur during the joining process, such as the formation of air pockets and how they behave once formed. Furthermore, optimized application pattern and the merging behaviour of different flow fronts are considered. In total, four different adhesive application patterns were investigated experimentally and analysed using Particle Image Velocimetry. The investigations were complemented by a numerical simulation of the corresponding squeeze flow experiments — which achieved a high level of agreement. It was found that during the joining process, existing air pockets can reform due to the phenomenon of viscous fingering and subsequently cure into flat cavities, which are more critical in terms of bonding strength than spherical ones.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Mechanical Behavior of Composites · Adhesion, Friction, and Surface Interactions · Metal Forming Simulation Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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