journal article·2019-06

Adhesive wood joints under quasi-static and cyclic fatigue fracture Mode II loads

Gaspard Clerc, Andreas J. Brunner, Sébastien Josset, Peter Niemz, Frédéric Pichelin, Jan Willem G. Van de Kuilen

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Abstract

This paper investigates the energy release rate (ERR) in Mode II in-plane shear during delamination propagation under quasi-static and cyclic fatigue fracture loading with the 4-point end notched flexure (4-ENF) fracture test specimen. Wood joints bonded with three different adhesives, one rather brittle phenol resorcinol formaldehyde (PRF) and two different one component polyurethane (1C-PUR) adhesives with relatively low modulus of elasticity were tested in order to investigate the influence of the adhesive properties on the damage propagation under quasi-static and cyclic fatigue loading. A simple reduction method based on the specimens’ compliance was used to calculate the crack growth and the energy release rate during the test. Additionally, an automated analysis method was developed estimating the energy of crack initiation from quasi-static test results. This shall avoid introducing additional scatter due to operator-dependent, manual analysis. It was shown that the three tested adhesives are displaying similar ERR values under quasi-static loading. Under cyclic fatigue fracture loading, the more brittle PRF samples are showing a slower crack growth rate for similar energy release rate in comparison with the 1C-PUR adhesives. The proposed testing method, applied to adhesively bonded wood joints, has been shown to give satisfactory results. This can be used for the development of new adhesives with increased performance regarding fatigue delamination growth. The automated data analysis has potential for application on other materials under cyclic Mode II fatigue fracture loads.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Mechanical Behavior of Composites · Wood Treatment and Properties · Structural Response to Dynamic Loads

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

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