Application of ANN in the prediction of the pore concentration of aluminum metal foams manufactured by powder metallurgy methods

Abstract

In this work, the effect of fabrication parameters on the pore concentration of aluminum metal foam, manufactured by the powder metallurgy process, has been studied. The artificial neural network (ANN) technique has been used to predict pore concentration as a function of some key fabrication parameters. Aluminum metal foam specimens were fabricated from a mixture of aluminum powders (mean particle size 60 μm) and NaCl at 10, 20, 30, 40(wt)% content under a pressure of 200, 250, and 300 MPa. All specimens were then sintered at 630°C for 2.5 h in argon atmosphere. For pore formation (foaming), sintered specimens were immersed into 70°C hot running water. Finally, the pore concentration of specimens was recorded to analyze the effect of fabrication parameters (namely, NaCl ratio, NaCl particle size, and compacting pressure) on the foaming behavior of compacted specimens. It has been recorded that the above-mentioned fabrication parameters are effective on pore concentration profile while pore diameters remain unchanged. In the ANN training module, NaCl content (wt)%, NaCl particle size (μm), and compacting pressure (MPA) were employed as inputs, while pore concentration % (volume) of compacts related to fabrication parameters was employed as output. The ANN program was successfully used to predict the pore concentration % (volume) of compacts related to fabrication parameters.