  ANN (Artificial Neural Networks) modeling methodology was adopted for
predicting mechanical properties of aluminum cast composite materials. For this
purpose aluminum alloy were developed using conventional foundry method. The
composite materials have complex nature which posses the nonlinear relationship
among heat treatment, processing parameters, and composition and affects their
mechanical properties. These nonlinear relation ships with properties can more
efficiently be modeled by ANNs. Neural networks modeling needs sufficient data
base consisting of mechanical properties, chemical composition and processing
parameters. Such data base is not available for modeling. Therefore, a large
range of experimental work was carried out for the development of aluminum
composite materials. Alloys containing Cu, Mg and Zn as matrix were reinforced
with 1- 15% Al2O3 particles using stir casting method. Alloys composites were
cast in a metal mold. More than eighty standard samples were prepared for
tensile tests. Sixty samples were given solution treatments at 580oC for half
an hour and tempered at 120oC for 24 hours. The samples were characterized to
investigate mechanical properties using Scanning Electron Microscope, X-Ray
Spectrometer, Optical Metallurgical Microscope, Vickers Hardness, Universal
Testing Machine and Abrasive Wear Testing Machine. A MLP (Multilayer
Perceptron) feedforward was developed and used for modeling purpose. Training,
testing and validation of the model were carried out using back propagation
learning algorithm. The modeling results show that an architecture of 14 inputs
with 9 hidden neurons and 4 outputs which includes the tensile strength,
elongation, hardness and abrasive wear resistance gives reasonably accurate
results with an error within the range of 2-7 % in training, testing and
validation.
