Determination of the stress-rupture characteristics of heat-resisting materials with long operating lives

1969 ◽  
Vol 1 (6) ◽  
pp. 569-574
Author(s):  
I. I. Trunin
Keyword(s):  
Stress Rupture ◽  
Heat Resisting Materials

Methodology to Predict Delayed Failure Due to Slow Crack Growth in Ceramic Tubular Components Using Data From Simple Specimens

1993 ◽  
Vol 115 (2) ◽  
pp. 204-210 ◽  
Author(s):  
O. M. Jadaan ◽  
R. E. Tressler
Keyword(s):  
Crack Growth ◽  
Fatigue Testing ◽  
Slow Crack Growth ◽  
Stress Rupture ◽  
Scaling Analysis ◽  
Rupture Data ◽  
Threshold Stress Intensity Factor ◽  
Using Data ◽  
Tubular Components

The methodology to predict the liftime of sintered α-silicon carbide (SASC) tubes subjected to slow crack growth (SCG) conditions involved the experimental determination of the SCG parameters of that material and the scaling analysis to project the stress rupture data from small specimens to large components. Dynamic fatigue testing, taking into account the effect of threshold stress intensity factor, of O-ring and compressed C-ring specimens was used to obtain the SCG parameters. These SCG parameters were in excellent agreement with those published in the literature and extracted from stress rupture tests of tensile and bend specimens. Two methods were used to predict the lifetimes of internally heated and pressurized SASC tubes. The first is a fracture mechanics approach that is well known in the literature. The second method used a scaling analysis in which the stress rupture distribution (lifetime) of any specimen configuration can be predicted from stress rupture data of another.

Some Observations Regarding the Statistical Determination of Stress Rupture Regression Lines

1979 ◽  
Vol 101 (4) ◽  
pp. 286-291
Author(s):  
P. P. Pizzo
Keyword(s):  
Least Squares ◽  
Curve Fitting ◽  
Statistical Evaluation ◽  
Least Squares Method ◽  
Stress Rupture ◽  
Anomalous Behavior ◽  
Creep Data ◽  
Statistical Determination ◽  
Selection Of

Observations concerning the statistical evaluation of creep data are presented. Methods currently employed in the determination of stress rupture regression lines can result in conflicting and necessarily invalid results. Anomalous behavior is principally associated with the selection of the dependent variable. However, it is the least squares method of curve fitting which introduces regression bias. Methods to improve the validity of least squares regressions are suggested.

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