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Statistical Analysis of Slow Crack Growth Experiments

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84138

Pfingsten,  T
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Pfingsten, T. (2006). Statistical Analysis of Slow Crack Growth Experiments. Journal of the European Ceramic Society, 26(15), 3061-3065. doi:doi:10.1016/j.jeurceramsoc.2005.08.004.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CF85-5
Abstract
A common approach for the determination of Slow Crack Growth (SCG) parameters are the static and dynamic loading method. Since materials with small Weibull module show a large variability in strength, a correct statistical analysis of the data is indispensable. In this work we propose the use of the Maximum Likelihood method and a Baysian analysis, which, in contrast to the standard procedures, take into account that failure strengths are Weibull distributed. The analysis provides estimates for the SCG parameters, the Weibull module, and the corresponding confidence intervals and overcomes the necessity of manual differentiation between inert and fatigue strength data. We compare the methods to a Least Squares approach, which can be considered the standard procedure. The results for dynamic loading data from the glass sealing of MEMS devices show that the assumptions inherent to the standard approach lead to significantly different estimates.