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Assessing the reliability of a nanocomponent using nanocrack growth.
- Source :
- Applied Stochastic Models in Business & Industry; May2014, Vol. 30 Issue 3, p294-302, 9p
- Publication Year :
- 2014
-
Abstract
- In nanoscience and nanotechnology, much attention has been given to the dual problem of designing nanocomponents with novel physical properties and how such nanocomponents can be fabricated. Receiving less attention has been the question of the nanocomponent's reliability; how does a nanocomponent fail and how long does a nanocomponent survive under typical operating conditions? High reliability is necessary to guarantee the advancement and utilization of nanocomponents due to the fact that they account for a high proportion of costs of newly designed nanosystems as well as multiscale systems. A nanocomponent is a component that is made of atoms, and its reliability is determined by these atoms. There are situations where it is hard or impossible to extract information from a nanocomponent about its relationship to its atoms. In this article, we assess the nanocomponent's reliability by using its physical properties. Specifically, it is known that nanocrack growth involves considerable statistical variability and such variability should be accounted for assessing growth. In this paper, we first provide a stochastic nanocrack growth model and then evaluate the reliability of a nanocomponent based on this model. Various properties of this model are obtained. We also evaluate the reliability of a nanocomponent under different assumptions on our proposed growth model. This paper is a modification of the extensive literature on modeling fatigue cracks in materials on a larger scale, applied to nanoscale where growth is not a function of cumulative stress on the component but related to the time to first exceedance of a threshold. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15241904
- Volume :
- 30
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Applied Stochastic Models in Business & Industry
- Publication Type :
- Academic Journal
- Accession number :
- 96443333
- Full Text :
- https://doi.org/10.1002/asmb.1974