Mario Acosta Flores

By Mario Acosta Flores, Eusebio Jiménez López and Marta Lilia Eraña Díaz

The study of the mechanical behavior of composite materials has acquired great importance due to the innumerable number of applications in new technological developments. As a result, many theories and analytical models have been developed with which its mechanical behavior is predicted; these models require knowledge of elastic properties. This work describes a basic theoretical framework, based on linear elasticity theory and classical lamination theory, to generate constitutive models of laminated materials made up of orthotropic layers. Thus, the models of three orthotropic laminated composite materials made up of layers of epoxy resin reinforced with fiberglass were also obtained. Finally, by means of experimental axial load tests, the constants of the orthotropic layers were determined.

Part of the book: Elasticity of Materials

By Marta Eraña-Díaz and Mario Acosta-Flores

This chapter explores the use of an artificial neural network (ANN) to obtain the elastic constants of the components of a metal laminated composite material (MLCM). The dataset for the training and validation of the ANN was obtained by applying an analytical model developed for the study of stresses in MLCM. The information used in the dataset corresponds to MLCM configurations and data generated with variants registered in the structural presentation of the inputs and outputs. The best configuration found for the generation of the ANN models yielded an average relative error of less than 4% in relation to the results of the constants evaluated and published in a previous article. As shown in this research, it is important to have a clear definition of the problem as well as an effective selection and preparation of the characteristics of the training data during the constitutive modeling of composite materials and the correct application of the ANN.

Part of the book: Artificial Neural Networks