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Evaluation of Fatigue Life Regression Models
Technical Paper
2004-01-0625
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Fatigue life regression models with constant and non-constant variance are evaluated and compared with a Random Fatigue Limit Model and a Probit model to estimate the fatigue strength and S-N relationship from fatigue test data. The Maximum Likelihood method is used to estimate parameters of the above models. Emphasis also is given to assessing the variation in fatigue strength and S-N relationships estimated from different models. Two different test data sets are selected for the evaluations: (1) a full S-N test data set with a large range of stresses and (2) a staircase test data set with a characteristically more narrow range of stresses. Model adequacies are evaluated from residuals and Anderson Darling measures of their fits. The Random Fatigue Limit Model is observed to best fit the test data, but its large number of parameters are under constrained with staircase test data and require care to get good results.
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Citation
Awad, M., DeJack, M., and Krivtsov, V., "Evaluation of Fatigue Life Regression Models," SAE Technical Paper 2004-01-0625, 2004, https://doi.org/10.4271/2004-01-0625.Also In
Fatigue Research and Applications, and Fatigue Analysis and Creative Problem Solving
Number: SP-1839; Published: 2004-03-08
Number: SP-1839; Published: 2004-03-08
References
- Bannantine, J. Comer, J. Handrock, J. Fundamentals of Metal Fatigue Analysis Prentice Hall
- Lu, M. Leiman, J. E. Rudy, R. J. Lee, Yung Step-Stress Accelerated Test Method- A Validation Study Society of Automotive Engineers, 2003-01-0470
- Meeker, W. Q. Escobar, L. A. Statistical Methods for Reliability Data John Wiley 1999
- Pascual, F. G. Meeker, W. Q. Estimating Fatigue Curves with the Random Fatigue-Limit Model Technometrics November 1999 VOL. 41 4
- Boileau, J. M. The Effect of Solidification Time on the Mechanical Properties of a Cast 319 Al Alloy PhD Thesis Wayne State Univ. Detroit, MI 2000
- Caton, M. J. Predicting Fatigue Properties of Cast Aluminum by Characterizing Small-Crack Propagation Behavior PhD Thesis University of Michigan Ann Arbor, MI 2001
- Mathmatica, Linear programming, statistics, optimization, combinatorics and graphic software Wolfram research Inc.
- Meeker, W. Q. SPLIDA Splus GUI functions for life data analysis 2000 http://www.public.iastate.edu/~wqmeeker/splida/betasplida.html
- Nelson, W. Fitting of Fatigue Curves with Nonconstant Standard Deviation to Data with Runouts Journal of Testing and Evaluation Vo. 12 2 March 1984 69 77
- Hahn, G. J. Morgan, C. B. Nelson, W. B. Early Failure Estimates from Low Cycle Fatigue Data Which are Insensitive to the Results from Long Life Units General Electric Co. Corp. Research and Development Schenectady, NY
- Buckley, J. J. James, I. R. Linear Regression with Censored Data Biometrica 66 1979 429 36
- Annis, Charles Excel spreadsheet with RFL model implemented with a macro http://www.statisticalengineering.com/
- Nelson, W. Analysis of Residuals from Censored Data Technometrics November 1973 VOL. 15 4
- Lipson, C. Sheth, N. Statistical Design and Analysis of Engineering Experiments McGraw-Hill
- Bolla, G. A. Accelerated Useful Life and Field Correlation Methods Society of Automotive Engineers, 2002-01-1175
- Klyatis, L. M. The strategy of Accelerated Reliability Testing Development for Car Components Society of Automotive Engineers, 2000-01-1195
- Nelson, W. Accelerated Testing: Statistical Models, Test Plans, and Data Analysis John Wiley & Sons New York, NY 1990