Wear performance analysis of Aluminum matrix composites using Artificial neural network

This paper represents the study wear characteristics of AA5083/SiC micro and nanocomposite using the artificial neural network. The aluminum matrix composites with different wt % of SiC micro (5% and 10%) and nanoparticles (1% and 2%) were fabricated using stir casting route. The gears were fabricated using developed MMC and tested under wear application at room temperature. The rotation of the gears during the wear test was kept constant to 1450 rpm. The wear performance of gears was investigated with four levels of experiment time (30, 60, 90 and 120mins) and three levels of applied load (10N, 20N, and 30N). The response for the test was wear wt. (%). The wear (%) obtained for each MMC was used to train the neural network for evaluating the performance and prediction capability of the model for AMCs reinforced with micro- and nanoparticles using MATLAB's neural network toolbox. The best validation performance of the network was obtained at 12th (5.8×10-8) and 28th (9.1×10-8) epoch for micro and nano particles composite respectively. The MSE for the predicted regression plots was found to be 2.0×10-4 and 1.2×10-8 for micro and nano-particles composite data. Furthermore, the experimental and predicted results compared to check the accuracy of the network.