Measurements and models for design with modern adhesives
Source abstract
Abstract
Abstract In many applications, adhesively bonded joints are increasingly required to sustain large deformations before failing and modern adhesives are formulated to provide this performance. There is a growing trend for the use of predictive design calculations, such as finite element modelling, to replace prototyping in the product development cycle. However, for modern non-linear adhesives, the modelling and data requirements are not straightforward. Research under the DTI Performance of Adhesive Joints programme has sought to improve understanding of these requirements for rubber-toughened structural adhesives and flexible adhesives. Toughened adhesives are elastic–plastic materials whose behaviour can be characterised at large strains by plasticity theory. The yield stress for rubber-toughened polymers is influenced by cavitation of the toughening phase in the glassy matrix. Functions are proposed to predict the contribution from this yield mechanism, and experimental methods to obtain the required parameters are outlined. Comparisons with test data show that the cavitation model accurately predicts the behaviour of these complex materials. Large strain elastic properties are required to characterise the behaviour of rubber-like flexible adhesives. Such behaviour can be modelled using hyperelastic material models developed for natural and synthetic rubbers. Available evidence suggests that first-order hyperelastic models show most promise. The results shown in this work suggest that the material property data required to model flexible adhesive behaviour can be obtained from uniaxial tension tests without recourse to the full set of tests specified for rubbers.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Elasticity and Material Modeling · Mechanical Behavior of Composites · Engineering and Materials Science Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (12)
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