On the influence of porosity and pore size on AlSi17 alloy foam using artificial neural network

In the present investigation, AlSi17 Aluminum alloy closed-cell foam is fabricated through Melt route process using Calcium powder as thickening agent and Titanium hydride as foaming agent along with the addition of 10wt% Silicon Carbide particles. The effect of pore and pore size on the deformation mechanism under static loading conditions is studied. Also, the fabricated foam properties are analyzed after the completion of the test. The strain rate loading conditions of the compression test conducted on the Al foam lies in the range of 10^-3s^-1 to 10s^-1 and the above investigations are carried out according to the loading conditions. The Artificial Neural Artwork (ANN) approach is employed for predicting the compressive deformation of the fabricated Al alloy foam using simulations. The Plateau stress data is obtained from the compression tests and the neural network functions are successively modeled and later the specific energy absorption (SEA) is calculated from the plateau stress. The simulation results of the ANN are in good agreement with the compression test results and the predictions are performed with highest accuracy.