Modeling Charpy impact behavior of Al6061/SiCp laminated nanocomposites by genetic programming

Abstract

In this work, 4 different gene expression programming models were conducted to predict Charpy impact energy of Al6061-SiCP laminated nanocomposites produced by mechanical alloying. The differences between the models were in their number of genes, head size and chromosomes as well as their linking function. To build the models, 171 pair input-target data were gathered from the literature, randomly divided into 133 and 38 data sets and then were respectively trained and tested by the proposed models. 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 were 6 independent input parameters. The output parameter was Charpy impact energy of the laminated nanocomposites. Although the entire models proposed high performance outcomes, the best performance model had the absolute fraction of variance, the mean absolute percentage error and the root mean square error of 0.9826, 10.217 and 12.432, respectively. All of the training and testing results in the models showed an appropriate performance for predicting Charpy impact energy of Al6061/SiCp laminated nanocomposites in the considered range.