Predicting Charpy impact energy of Al6061/SiCp laminated nanocomposites in crack divider and crack arrester forms

Charpy impact energy of the produced Al6061–SiCp laminated nanocomposites by mechanical alloying was modeled by adaptive neuro-fuzzy interfacial systems (ANFIS) in both crack divider and crack arrester configurations. The model was constructed by training, validating and testing of 171 gathered input–target data. The thickness of layers, the number of layers, the adhesive type, the crack tip configuration and the content of SiC nanoparticles were five independent input parameters utilized for modeling. The output parameter was Charpy impact energy of the nanocomposites. The performance of the proposed models was evaluated by absolute fraction of variance, the absolute percentage error and the root mean square error and the best values of 0.9945, 3.521 and 8.224, respectively acquired for them. The results introduced ANFIS as an influential tool for predicting the Charpy impact energy of the considered Al6061–SiCp laminate nanocomposites.