مقاله انگلیسی رایگان در مورد الگوریتم ژنتیکی برای توصیف مواد هایپرالاستیک – الزویر 2018

Hyperelastic constitutive laws are used to model materials that respond elastically when subjected to very large strains. They account both for nonlinear material behaviour and large shape changes. The main applications of the theory are to model the rubbery behaviour of a polymeric material, and to model polymeric foams that can be subjected to large reversible shape changes (e.g. a sponge). Even if this kind of materials is widely used in industry, the difficulties to obtain an accurate mathematical characterization limit the possibilities to generate feasible numerical models that predict their behaviour. As a consequence, the virtual Finite Element (FE) models using hyperelastic materials are in some cases unable to reach accurate results. Even if nowadays there exist a good number of models (see, e.g., [1,2,5,7,8,17–19,21]), theirs material constants usually are not simple to be obtained. The simple test performed in order to find the material data is a uniaxial tensile test [24]. Once the test is carried out, the obtained results are fitted by a three-parameter Mooney–Rivlin model [16,23] using a fully incompressible assumption for the material [1,15]. The first challenge in this work is to get an accurate model characterization for a complex material using only simple tests on a specimen. Once the characterization is obtained, it will be used in a FE code in order to solve a more complex problem involving different stress states. In order to solve characterization problems, several different methods are available to find the value of parameters in hyperelastic models. Most of authors use least square fitting methods [13], very often based on iterative processes which require an initial data to initialize the method and also differentiation of the objective function. In our case, we use Genetic Algorithms (GA) to perform the searching of parameters in order to characterize the material. This technique does not require differentiation and, although there are other available and more sophisticated methods which do not require it [26], GA are methods oriented to global optimization and are less sensitive to initialization as other techniques. Moreover, using GA we do not need to provide an initial data for the parameters to run the algorithm, although a parametric domain must be defined. Finally, using this method we are able to include multi-objective optimization if needed (for example in order to use different tests for the characterization) in a very simple way.