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ANN (Artificial Neural Networks) modeling methodology was adopted forpredicting mechanical properties of aluminum cast composite materials. For thispurpose aluminum alloy were developed using conventional foundry method. Thecomposite materials have complex nature which posses the nonlinear relationshipamong heat treatment, processing parameters, and composition and affects theirmechanical properties. These nonlinear relation ships with properties can moreefficiently be modeled by ANNs. Neural networks modeling needs sufficient database consisting of mechanical properties, chemical composition and processingparameters. Such data base is not available for modeling. Therefore, a largerange of experimental work was carried out for the development of aluminumcomposite materials. Alloys containing Cu, Mg and Zn as matrix were reinforcedwith 1- 15% Al2O3 particles using stir casting method. Alloys composites werecast in a metal mold. More than eighty standard samples were prepared fortensile tests. Sixty samples were given solution treatments at 580oC for halfan hour and tempered at 120oC for 24 hours. The samples were characterized toinvestigate mechanical properties using Scanning Electron Microscope, X-RaySpectrometer, Optical Metallurgical Microscope, Vickers Hardness, UniversalTesting Machine and Abrasive Wear Testing Machine. A MLP (MultilayerPerceptron) feedforward was developed and used for modeling purpose. Training,testing and validation of the model were carried out using back propagationlearning algorithm. The modeling results show that an architecture of 14 inputswith 9 hidden neurons and 4 outputs which includes the tensile strength,elongation, hardness and abrasive wear resistance gives reasonably accurateresults with an error within the range of 2-7 % in training, testing andvalidation.

Modeling Mechanical Properties of Aluminum Composite Produced Using Stir Casting Method