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Effects of Cracking Test Conditions on Estimation Uncertainty for Weibull Parameters Considering Time-Dependent
Jae Phil Park1, Chanseok Park2, Jongweon Cho3
1School of Mechanical Engineering, Pusan National University, Busan 46241, Korea. jppark@pusan.ac.kr.
Materials (Basel, Switzerland)
|August 5, 2017
Summary
Predicting crack initiation is challenging. This study uses Monte Carlo simulations to quantify how experimental conditions, like censoring interval length, affect Weibull model parameter estimation uncertainty for crack initiation.
Area of Science:
- Materials Science
- Mechanical Engineering
- Statistical Modeling
Background:
- Predicting crack initiation time is difficult due to wide experimental time spreads.
- Probabilistic models, like the Weibull distribution, are commonly used for crack initiation time.
- Estimating Weibull parameters requires data from cracking tests, but assessing experimental condition effects on uncertainty is challenging.
Purpose of the Study:
- To investigate the impact of key experimental conditions on Weibull parameter estimation uncertainty.
- To quantify the time-dependent effect of censoring interval length on model estimation uncertainty.
- To provide insights for optimizing experimental designs for probabilistic crack initiation modeling.
Main Methods:
- Utilized Monte Carlo simulations to model crack initiation.
- Investigated the influence of varying experimental conditions, including censoring interval length.
- Analyzed the estimation uncertainties of Weibull distribution parameters.
Main Results:
- Simulation results provided quantified estimation uncertainties for Weibull parameters.
- Demonstrated the impact of experimental conditions on the reliability of probabilistic crack initiation models.
- Identified specific effects of censoring interval length on parameter estimation.
Conclusions:
- Experimental conditions significantly influence the uncertainty in Weibull parameter estimation for crack initiation.
- The study offers quantified insights into optimizing experimental designs for accurate probabilistic modeling.
- Findings aid researchers in developing more reliable crack initiation models through informed experimentation.
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