Application of ANN Back-Propagation for Fracture Design Parameters of Medium Carbon Steel in Extra-Low Cycle Axial Fatigue Loading
The fracture problems of medium carbon steel under extra-low cycle axial fatigue loading were studied using artificial neural network in this paper. The training data were used in the formation of training set of artificial neural network. The artificial neural network model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. Training artificial neural network model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, notch depth and tip radius of the notch, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The artificial neural network model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. The result show that the training model has good performance, and the experimental data and predicted data from artificial neural network are in good coherence.
S.W. Nam, Y.W. Chang, S.B. Lee and N.J. Kim
H. Y. Duan et al., "Application of ANN Back-Propagation for Fracture Design Parameters of Medium Carbon Steel in Extra-Low Cycle Axial Fatigue Loading ", Key Engineering Materials, Vols. 345-346, pp. 445-448, 2007