Application of ANN Back-propagation for Fracture Design Parameters in Extra-low Cycle Rotating Bending Fatigue
The fracture problems of ecomaterial (aluminum alloyed cast iron) under extra-low cycle rotating bending fatigue loading were studied using artificial neural networks (ANN) in this paper. The training data were used in the formation of training set of ANN. The ANN model exhibited excellent in results 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 ANN 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, presetting deflection and tip radius of the notch, and the output parameters, the cycle times of fracture were used during the network training. The ANN 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. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.
Artificial Neural Networks Extra-low Cycle Ecomaterial (aluminum alloyed cast iron) Fatigue Fracture design
Hongyan DUAN Youtang LI Jin ZHANG Guiping HE
Key Laboratory of Digital Manufacturing Technology and Application, The Ministry of Education,Lanzhou University of Technology, Lanzhou , 730050, China School of Mechanical and Electronical Engineering, Lanzhou University of Technology,Lanzhou , 730050, C
国际会议
重庆
英文
450-453
2008-06-09（万方平台首次上网日期，不代表论文的发表时间）