Abstract
In this article, Charpy impact resistance of aluminum–epoxy-laminated composites in both crack divider and crack arrester configurations has been investigated. In both configurations, an analytical investigation has been carried out to evaluate the effects of layer thickness on impact resistance of the specimens. A model based on artificial neural networks for predicting impact resistance of the specimens has been presented. For the purpose of building the model, training and testing usingexperimental results from 126 specimens produced from two basic composites were conducted. The data used in the multilayer feed forward neural networks models are arranged in a format of seven input parameters that cover the thickness of layers,the number of layers, the adhesive type, the crack tip configuration, the content of SiC particles, the content ofmethacrylated butadiene–styrene particles, and the test trial number. According to these input parameters, in the neuralnetworks model, the impact resistance of each s...
Published Version
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