forecasting the tension of casting aluminum parts using artificial neural network

as aluminum alloys have high strength to weight ratio, resistance to abrasion and corrosion and good electrical and thermal conductivity, are suitable for special attention in the industry nowadays, many aluminum castings parts are commercial alternative for other parts several factors have played a role in the quality of the casting produced that review and study of experimental in industrial scale, requires much time and cost. one of the ways that had known to accurately predict the behavior of these components, is usage of neural network theory. in this study, stress of casting aluminum parts has been studied and predicted by artificial neural networks due to factors such as: melting temperature of the chemical composition. training artificial neural network was carried through multilayer perceptron procedure. the comparison shows the proper implementation of the predicted values and experimental results and confirm the effectiveness of the method used.