• CN：11-2187/TH
• ISSN：0577-6686

• 材料科学与工程 •

### 基于相对误差平方和的神经网络预测镁合金多轴疲劳寿命

1. 浙江工业大学机械工程学院 杭州 310032
• 出版日期:2016-02-15 发布日期:2016-02-15
• 作者简介:熊缨(通信作者),女,1969年出生,教授,博士研究生导师。主要研究方向为材料的疲劳与损伤。E-mail：yxiong@zjtu.edu.cn;岑恺,男,1989年出生,硕士研究生。主要研究方向为镁合金疲劳和神经网络。E-mail：nbcxck@163.com
• 基金资助:
国家自然科学基金(51275472)和浙江省自然科学基金(LY12E05024)资助项目

### Neural Network Based on Sum Squared Relative Error to Predict the Multixial Fatigue Life of Magnesium Alloy

XIONG Ying, CEN Kai

1. College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310032
• Online:2016-02-15 Published:2016-02-15

Abstract: An improved BP network which use sum of squared relative error (SSRE) as the performance function is applied to predict the fatigue life of three kinds of magnesium alloy under different loading paths. Stain-controlled fatigue experiments are conducted on AZ31B and ZK60 magnesium alloy under four loading paths, which including fully reversed tension-compression, cyclic torsion, 45° in-phase axial-torsion and 90° out-of-phase axial-torsion. In addition, the fatigue data of AZ61A magnesium alloy from literature are also adopted. Two fatigue life prediction methods, namely, a standard BP network which use mean squared error(MSE) as the performance function, and Smith-Watson-Topper(SWT) critical plane fatigue models, are evaluated based on the experimentally obtained fatigue results. Result shows that all of the predicted results except one date by both BP network are within factor-of-three boundaries, there are 16 date, 13 date and 10 date predicted by SWT model outside factor-of-three boundaries even factor-of-five boundaries, respectively. Both BP network are found to be able to correlate the fatigue experiments reasonably well in comparison with SWT model, and SSRE-BP network is better than MSE-BP network.