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基于缺陷概率特点的粉末冶金材料寿命预测概率模型
引用本文:魏大盛,杨晓光,王延荣.基于缺陷概率特点的粉末冶金材料寿命预测概率模型[J].航空动力学报,2005,20(6):951-957.
作者姓名:魏大盛  杨晓光  王延荣
作者单位:北京航空航天大学能源与动力工程学院,北京100083
摘    要:粉末冶金材料由于其制造工艺的特点,强度和寿命对微缺陷(夹杂、气孔、表面划伤)十分敏感,导致了粉末冶金材料的破坏具有较大的分散性,使得寿命预测更为困难。本文根据国内粉末材料中缺陷的分布特点,基于文献79]中概率断裂分析的思路,对原有方法进行了修正和推广,重新给出了缺陷在表面、亚表面以及内部时的定义及缺陷在这些不同位置时出现概率断裂的确定方法,建立了一个可考虑缺陷形状、大小、位置等分布特征的粉末冶金材料寿命预测概率模型,并基于此计算了不同尺寸缺陷位于不同位置时材料的失效概率及总失效概率。分析表明:所给出的方法可以很好的表征国内工艺条件下粉末冶金材料缺陷的概率特征对强度寿命的影响,方法是有效的。 

关 键 词:航空、航天推进系统    粉末冶金    缺陷    临界尺寸    概率
文章编号:1000-8055(2005)06-0951-07
收稿时间:2004/11/29 0:00:00
修稿时间:2004年11月29

A Probabilistic Model for Prediction of Life Based on Probabilistic Character of Defects in PM Alloys
WEI Da-sheng,YANG Xiao-guang and WANG Yan-rong.A Probabilistic Model for Prediction of Life Based on Probabilistic Character of Defects in PM Alloys[J].Journal of Aerospace Power,2005,20(6):951-957.
Authors:WEI Da-sheng  YANG Xiao-guang and WANG Yan-rong
Institution:School of Jet Propulsion,Beijing University of Aeronautics and Astronautics,Beijing100083,China
Abstract:Due to manufacturing characteristic,the strength and life of powder metallurgy alloy are sensitive to micro defects,such as nonmetallic inclusion,pores,and nicks on the surface.A distinct character of failure of this kind of material is scatter,which makes life prediction more difficult.In this paper,based on the defect populations of domestic PM alloy and some probability failure methods established in reference ,definition of the probability for the presence of a defect inside the surface,sub-surface and internal region is given first,and then a probabilistic model is set up,which can calculate the failure probability caused by defects having different shapes and different sizes,being in different regions,and also can calculate the total failure probability.It is concluded that the method can describe the effect of probabilistic character of defects on the strength and life of PM alloy,and it is proved to be valid.
Keywords:aerospace propulsion system  powder metallurgy  defects  critical size  probabilistic
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