- Title
- S-N curve models for composite materials characterisation: an evaluative review
- Creator
- Burhan, Ibrahim; Kim, Ho Sung
- Relation
- Journal of Composites Science Vol. 2, Issue 3, no. 38
- Publisher Link
- http://dx.doi.org/10.3390/jcs2030038
- Publisher
- MDPI AG
- Resource Type
- journal article
- Date
- 2018
- Description
- S-N behavior has been a backbone of material fatigue life studies since the 19th century. Numerous S-N curve models have been produced but they have been arbitrarily chosen in numerous research works dominantly for composite materials. In this paper, they were critically reviewed and evaluated for capability using the following criteria: data fitting capability, efficiency of curve fitting, applicability to data sets at various stress ratios (−0.43, −1, −3, 0.1, and 10), representability of fatigue damage at failure, and satisfaction of the initial boundary condition. The S-N curve models were found to be in two categories—one for fatigue data characterization independent of stress ratio, and the other for those designed for predicting the effect of stress ratio. The models proposed by Weibull, Sendeckyj, and Kim and Zhang for fatigue data characterization appeared to have the best capabilities for experimental data obtained from Weibull for R = −1, from Sendeckyj for R = 0.1, and from Kawai and Itoh (for R = −0.43, −3, and 10). The Kim and Zhang model was found to have an advantage over the Weibull and the Sendeckyj models for representing the fatigue damage at failure. The Kohout and Vechet model was also found to have a good fitting capability but with an inherent limitation for shaping the S-N curve at some stress ratios (e.g., R = −0.43). The S-N curve models developed for predicting the effect of stress ratio were found to be relatively inferior in data fitting capability to those developed directly for fatigue data characterization.
- Subject
- S-N curve; model; curve fitting capability; curve fitting efficiency; fatigue damage; stress ratio
- Identifier
- http://hdl.handle.net/1959.13/1393231
- Identifier
- uon:33504
- Identifier
- ISSN:2504-477X
- Rights
- © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
- Language
- eng
- Full Text
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