摘要
提出一种基于Matlab的BP人工神经网络的多因素疲劳寿命预测方法,建立了缺口轴(缺口半径、缺口张开角和缺口深度)的神经网络疲劳寿命估算模型,模型的预测结果与试验结果吻合较好,表明:这种预测方法具有较高的精度、良好的自适应和自学习的智能化特征等优点,克服了传统计算方法计算量大,要依赖于数学模型的缺点。
A kind of fatigue life prediction method was proposed based on the Matlab BP artifical neural networks.The fatigue life prediction model of gap axis has been established,about gap radius,gap depth and gap opening angle.The simulation results show that this method indicater the advantage of high precision,good adaptive and self-learning intelligent characteristics to predict the gap shaft fatigue life on gap parameters(gap radius,gap depth and gap opening angle).The calculation method overcomes the traditional shortcomings that rely on the mathematical model and a large quantity of calculation.
出处
《稀有金属材料与工程》
SCIE
EI
CAS
CSCD
北大核心
2013年第S2期549-552,共4页
Rare Metal Materials and Engineering
基金
国家自然科学基金资助(51065014)
甘肃省自然科学基金资助(1112RJZA004)
关键词
低周疲劳
缺口轴
弯扭
寿命预测
low cycle fatigue
gap axis
bending and torsion loads
life prediction
