Application of ANN back-propagation for fracture design parameters of middle carbon steel in extra-low cycle bend torsion loading

doi:10.4028/www.scientific.net/AMM.345.272

CORC > 兰州理工大学 > 兰州理工大学 > 学报编辑部

	Application of ANN back-propagation for fracture design parameters of middle carbon steel in extra-low cycle bend torsion loading
	Duan, HongYan; Li, YouTang; Sun, ZhiJia; Zhang, YangYang
	2013
会议日期	June 22, 2013 - June 23, 2013
会议地点	Wuhan, China
关键词	Backpropagation Carbon steel Industrial engineering Mechanical engineering Network architecture Neural networks Torsional stress Extra-low cycle Middle carbon steels Neural network model Number of hidden neurons Performance of systems Prediction techniques Torsion fatigue Trained neural networks
卷号	345
DOI	10.4028/www.scientific.net/AMM.345.272
页码	272-276
英文摘要	The fracture problems of medium carbon steel (MCS) under extra-low cycle bend torsion 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 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. It is possible to claim that, ANN is fairly promising prediction technique if properly used. 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 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 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. © (2013) Trans Tech Publications, Switzerland.
会议录	Applied Mechanics and Materials
会议录出版者	Trans Tech Publications Ltd, Kreuzstrasse 10, Zurich-Durnten, CH-8635, Switzerland
语种	英语
ISSN号	16609336
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/117610]
专题	学报编辑部机电工程学院
作者单位	College of Mechano-Electronic Engineering, Lanzhou University of Technology, Lanzhou 730050, China
推荐引用方式 GB/T 7714	Duan, HongYan,Li, YouTang,Sun, ZhiJia,et al. Application of ANN back-propagation for fracture design parameters of middle carbon steel in extra-low cycle bend torsion loading[C]. 见:. Wuhan, China. June 22, 2013 - June 23, 2013.