• Journal of Ocean Engineering and Technology
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• v.21 no.4
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• pp.28-33
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• 2007

The objective of this paper is to investigate the relationship between the short-term creep rupture time and the creep rupture properties at three different elevated temperatures in STS304 stainless steel. Uniaxial constant stress creep rupture tests were performed on the steel to observe the creep rupture behaviors at the elevated temperatures of 600, 650 and 700, according to the testing matrix. It is very important to predict creep life in practical creep design problems. As one of the series of studies on the statistical modelling of probabilistic creep rupture time and the development of creep life prediction techniques, the relationship between applied stress and creep rupture behaviors, such as creep strain rate and rupture time, were investigated. In addition, the Monkman-Grant relationship was observed between the steady-state creep rate and the creep rupture time. The creep rupture surfaces observed by SEM showed up dimple phenomenon at all conditions.

• Journal of Ocean Engineering and Technology
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• v.24 no.6
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• pp.97-102
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• 2010

This paper deals with the variability of short term creep rupture time based on previous creep rupture tests and the statistical methodology of the creep life prediction. The results of creep tests performed using constant uniaxial stresses at 600, 650, and $700^{\circ}C$ elevated temperatures were used for a statistical analysis of the inter-specimen variability of the short term creep rupture time. Even under carefully controlled identical testing conditions, the observed short-term creep rupture time showed obvious inter-specimen variability. The statistical aspect of the short term creep rupture time was analyzed using a Weibull statistical analysis. The effect of creep stress on the variability of the creep rupture time was decreased with an increase in the stress level. The effect of the temperature on the variability also decreased with increasing temperature. A long term creep life prediction method that considers this statistical variability is presented. The presented method is in good agreement with the Lason-Miller Parameter (LMP) life prediction method.