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In order to discover the most accurate prediction of yield stress, UTS and elongation percentage, the effects of various training algorithms on learning performance of the neural networks were investigated. Different primary and secondary dendrite arm spacings were used as inputs, and yield stress, UTS and elongation percentage were used as outputs in the training and test modules of the neural network. After the preparation of the training set, the neural network was trained using different training algorithms, hidden layers and neuron numbers in hidden layers. The test set was used to check the system accuracy of each training algorithm at the end of learning. The results show that Levenberg-Marquardt learning algorithm gave the best prediction for yield stress, UTS and elongation percentage of A356 alloy. K e y w o r d s: FEM, ANN, training algorithms

The most accurate ANN learning algorithm for FEM prediction of mechanical performance of alloy A356