Abstract
In this paper, genetic algorithm (GA) was used to enhance the accuracy of back propagation artificial neural network (ANN) for the tensile behaviour of 316L. With GA optimization, the random weight and threshold between different layers in original ANN model can be updated constantly to describe the characteristics of 316L austenitic stainless steel accurately, just considering the nodes of hidden layer. According to the sample distribution, the data were divided into training and test. When the model was set to predict test data, the results showed coincident with the experimental ones, and errors of the data between prediction and experiment displayed the small values at room and intermediate temperatures.
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