Modeling the low-velocity impact characteristics of woven glass epoxy composite laminates using artificial neural networks

Abstract

In this work, a new methodology based on artificial neural networks (ANN) has been developed to study the low-velocity impact characteristics of woven glass epoxy laminates of EP3 grade. To train and test the networks, multiple impact cases have been generated using statistical analysis of variance (ANOVA). Experimental tests were performed using an instrumented falling-weight impact-testing machine. Different impact velocities and impact energies on different thicknesses of laminates were considered as the input parameters of the ANN model. This model is a feed-forward back-propagation neural network. Using the input/output data of the experiments, the model was trained and tested. Further, the effects of the low-velocity impact response of the laminates at different energy levels were investigated by studying the cause-effect relationship among the influential factors using response surface methodology. The most significant parameter is determined from the other input variables through ANOVA.

An Artificial Neural Networks approach to predict low-velocity impact forces in an elastomer material

“…On this same basis, other studies present similar demonstrations of the effectiveness of ANNs to predict structural response and structural integrity on materials. [23][24][25][26] The use of ANNs for the study of impact phenomena in materials is still in development, and related investigations about the impact dynamics of rubber-like materials are currently rare even when neural networks-based models perform well in the study of the dynamic response of systems. [27][28][29] In this sense, ANNs could be of practical use to investigate the impact phenomenon in materials used primarily to reduce or absorb impact kinetic energy, such as rubber-like materials, without the explicit use of material parameters.…”