Viscoelastic characterisation of adhesives using inverse techniques
Lucie Rouleau  1@  , Jean-François Deü  1@  , Antoine Legay  1@  
1 : Laboratoire de Mécanique des Structures et des Systèmes Couplés  (LMSSC)  -  Website
Conservatoire National des Arts et Métiers (CNAM)
292, rue Saint Martin 75141 PARIS Cedex 03 -  France

Noise and vibration control is a major concern in several industries and a lot of work has been dedicated to the design of efficient active or passive damping treatments. Such treatments are usually applied to the vibrating structure by means of an adhesive layer. There is a high number of environmental parameters, such as temperature or frequency, which may influence the behaviour of the adhesive layer and modify the damping efficiency of the treatment. Therefore it is desired to model this bonding layer to take into account its viscoelastic behaviour in the finite element model.
 
The goal of this work is to present a procedure to characterise and model the adhesive layer. To that purpose, an experimental-numerical method for inverse characterisation of the frequency dependent properties of the adhesive layer is applied. The proposed inverse approach is based on a fractional derivative model whose parameters are identified by minimising the difference between the simulated and the measured dynamic response of a multi-layered structure assembled by bonding. The fractional derivative model presents the advantage of describing accurately the viscoelastic behaviour of many polymers with only four parameters. In the finite element model used for the optimisation, the adhesive layer is modeled by interface finite elements, i.e. by bidimensional elements representative of the tridimensional behaviour of the bonding layer.
 
The influence of the adhesive layer on the efficiency of a damping treatment is evidenced by performing dynamic testing on a sandwich structure with a viscoelastic core, assembled by bonding. The proposed characterisation and modeling procedure is applied in order to produce a better predictive model of the sandwich composite structure.


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