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

IR > 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
会议名称	2013 3rd International Conference on Mechanical Engineering, Industry and Manufacturing Engineering, MEIME 2013
会议录名称	Applied Mechanics and Materials
卷号	345
页码	272-276
会议日期	June 22, 2013 - June 23, 2013
会议地点	Wuhan, China
出版者	Trans Tech Publications Ltd, Kreuzstrasse 10, Zurich-Durnten, CH-8635, Switzerland
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.
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
DOI	10.4028/www.scientific.net/AMM.345.272
收录类别	EI
语种	英语
EI入藏号	20133616698290
EI主题词	Fracture
ISSN	16609336
来源库	Compendex
分类代码	912.1 Industrial Engineering - 723.4 Artificial Intelligence - 723 Computer Software, Data Handling and Applications - 722 Computer Systems and Equipment - 608 Mechanical Engineering, General - 545.3 Steel - 421 Strength of Building Materials; Mechanical Properties
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	会议论文
条目标识符	https://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
第一作者单位	兰州理工大学
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]:Trans Tech Publications Ltd, Kreuzstrasse 10, Zurich-Durnten, CH-8635, Switzerland,2013:272-276.