RETRACTED ARTICLE: Artificial neural networks for prediction Charpy impact energy of Al6061/SiC_p-laminated nanocomposites

In this work, Charpy impact energy of Al6061-SiC_p nanocomposites produced by mechanical alloying has been modeled by artificial neural networks in both crack divider and crack arrester configurations. Al6061 powders were mixed by 2, 3, and 5 vol % of SiC nanoparticles and ball-milled for 45 min. Afterward, the produced powders were hot-pressed in aluminum cans and then were extruded to produce a dense bulk. Charpy impact specimens were prepared from the produced samples in layered form with different adhesives and thickness of the layers. To build the model, training, validating, and testing was performed using 171 pair input-target data. The used data in the multilayer feed-forward neural networks models were arranged in a format of six input parameters including the thickness of layers, the number of layers, the adhesive type, the crack tip configuration, the content of SiC nanoparticles, and the test trial number. The output parameter was Charpy impact energy of the nanocomposites. The training, validating, and testing results in the neural network models have shown a strong potential for predicting Charpy impact energy of Al6061/SiC_p nanocomposites in the considered range of input-target values.