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Indexed by:Journal Papers

Date of Publication:2020-02-01

Journal:COMPOSITES PART B-ENGINEERING

Included Journals:EI、SCIE

Volume:182

ISSN No.:1359-8368

Key Words:Adhesive bonding; Environmental degradation; Nanoindentation; Hygrothermal ageing; Water diffusion

Abstract:Structural adhesives are being increasingly used for bonding of dissimilar materials, however environmental degradation remains a significant challenge limiting the bonding reliability. A common form of degradation comes from water ingress, regarding which there is limited quantitative understanding of how water diffusion affects the adhesive local mechanical properties. This work proposes a meso-scale approach to characterise the influence of water diffusion on local mechanical properties of structural adhesives at elevated temperature, aiming to develop a model of degradation due to water exposure. Gravimetric study was conducted on adhesives immersed in deionised and 5 wt% NaCl water, to obtain water diffusion characteristics. The immersed specimens were periodically removed from the aqueous environment and precisely cut to expose the internal section. The samples were then indented using nanoindentation to extract the modulus and hardness distribution. SEM observation was conducted to analyse the microscopic morphology and ageing mechanism. Experimental results revealed that water diffusion caused significant local (meso-scale) degradation in adhesive mechanical properties. Increase in local moisture concentration led to greater degradation as moisture gradually diffused inward. Comparing to salt water immersion, the elastic modulus and hardness of adhesive saturated in deionised water decreased by further 5.9% and 11.9%, respectively. The developed degradation model coupled with insights from ageing mechanism provides a detailed understanding of degradation in adhesive property due to water diffusion. The proposed characterisation approach can be readily applied to other adhesives. Furthermore, this model allows for degradation of such adhesives to be reasonably predicted through FE modelling effort.