Random cyclic stress-strain responses of a stainless steel pipe-weld metal II - a modeling

被引:15
作者
Zhao, YX
Gao, Q
Wang, JN
机构
[1] SW Jiaotong Univ, Inst Engn Mech, Chengdu 610031, Peoples R China
[2] SW Jiaotong Univ, Inst Appl Mech, Chengdu 610031, Peoples R China
基金
中国国家自然科学基金;
关键词
D O I
10.1016/S0029-5493(00)00242-9
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
This paper pays special attention to an issue that there is a significant scatter of the stress-strain responses of a nuclear engineering material, 1Cr18Ni9Ti stainless steel pipe-weld metal. Efforts are made to reveal the random fatigue damage character by fracture surface observations and to model the random responses by introducing probability-based stress-strain curves of Ramberg-Osgood relation and its modified form. Results reveal that the fatigue damage is subjected to, 3-D interacting and involved microcracks. The three stages, namely microstructural short cracks (MSC), physical short cracks (PSC) and long cracks (LC) subdivided by Miller and de los Rios, can give a good characterization of the damage process. Both micro- and macro-behaviour of the material have the character of three stages. The 3-D effects are strong in the MSC stage, tend to a gradual decrease in the PSC stage, and then show saturation after going to the LC stage. Intrinsic causes of the random behaviour are the difference and evolution of the microstructural conditions ahead of the dominant crack tips. The 'effectively short fatigue crack criterion' introduced by Zhao et al. in observing the material surface short crack behaviour could facilitate an understanding of the mechanism of interaction and evolution. Based on the previous obtained appropriate assumed distribution, normal model, for the cyclic stress amplitude, the probability-based curves are approximated by the mean value and standard deviation cyclic stress-strain curves. Then, fatigue analysis at arbitrarily given reliability can be conveniently made according to the normal distribution function. To estimate these curves, a maximum likelihood method is developed. The analysis reveals that the curves could give a good modeling of the random responses of material. (C) 2000 Elsevier Science S.A. All rights reserved.
引用
收藏
页码:315 / 326
页数:12
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