Viscoelastic materials have been widely used as devices for vibration control in general. Frequently, dynamic properties of those materials are provided by manufacturers only in a graph form in the frequency domain. This is a recurring problem in industry and academia. Thereby, the goal of this work is to contribute to this important issue which is to obtain the properties of viscoelastic materials from nomograms supplied by the manufacturer. The methodology is based on the digitalization of the nomogram of the material and on the subsequent reading of a set of points from two curves in different temperatures. An optimization problem with simple restrictions is built having characteristic constants of the constitutive models as design variables. The problem is solved by applying a hybrid optimization technique. The results obtained are presented, and prove to be very promising.

The use of viscoelastic materials (VEMs) has becoming more and more frequent both as vibration control in general or as parts of structural components. In all applications, the mechanical behavior of such materials can be predicted by the complex moduli (Young's, shear or volumetric) and the complex Poisson's ratio. Over recent decades, various methodologies have been presented aiming at characterizing complex moduli. On the other hand, the indirect identification of the Poisson's ratio, in the frequency domain, proves to be underexplored. The present paper discusses two computational methodologies in order to obtain, indirectly, the complex Poisson's ratio in linear and thermorheologically simple solid VEMs. The first of them uses a traditional methodology, which individually identifies the complex Young's and the shear moduli and, from them, one obtains the complex Poisson's ratio. The second methodologyproposed in the present paper and called 'integrated'-obtains the complex Poisson's ratio through a simultaneous identification of those two complex moduli. Both methodologies start from a set of experimental points of the complex moduli in the frequency domain, carried out at different temperatures. From those points, a hybrid optimization technique is applied (Genetic Algorithms and Non-Linear Programming) in order to obtain the parameters of the constitutive models for the VEM under analysis. For the experiments described here, the integrated methodology proves to be very promising and with a great application potential.

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