摘要
提出了用神经网络插值代替拉氏插值计算裂纹形成时间(TTCI)值,用极大似然估计代替均秩估计法估计分布参数的方法.考虑到仅对一组TTCI值进行参数估计具有较大的随机性,文中对每种参考裂纹尺寸对应的TTCI值均进行极大似然估计,得出多组TTCI分布参数;然后利用不同参考裂纹尺寸对应的TTCI分布参数之间的关系确定结构细节的当量初始缺陷尺寸分布参数.对某零件的疲劳实验及其原始疲劳质量分析证明了该方法的可行性和合理性.
An advanced description method of initial fatigue quality was proposed, in which neural network interpolation is employed instead of Lagrange interpolation to compute the values of the time to crack initiation （TTCI） and maximum likelihood estimation is used to estimate TTCI distribution parameters instead of mean rank estimation. Taking account into the randomicity encountered when only one group of TTCI values is used to estimate distribution parameters, several groups of TTCI distribution parameters were gained after maximum likelihood estimating for several groups of TTCI values corresponding to the given reference crack sizes. Then, equivalent initial flaw size distribution can be confirmed based on the relations of several groups of TTCI distribution parameters. The fatigue test of some component and its initial fatigue quality analysis show that the advanced method is feasible and reasonable.
出处
《北京科技大学学报》
EI
CAS
CSCD
北大核心
2006年第4期353-356,共4页
Journal of University of Science and Technology Beijing
基金
江苏省高技术研究资助项目(No.BG2004008)
关键词
断裂力学
原始疲劳质量
参数评估
极大似然估计
神经网络
插值
fracture mechanics
initial fatigue quality （IFQ）
parameter estimation
maximum likelihood estimation （MLE）
neural network
interpolation
