Rate Data and Material Model Parameter Estimation
Source abstract
Abstract
Traditionally stress-strain data are used to estimate material parameters in viscoplastic material models. Since the models exist in rate form, they must be integrated in time for the duration of the laboratory tests to give response models for parameter estimation. In this paper the alternative is considered in which the rate form of the material models is fitted to time differentiated stress strain data. Material parameteres, their 95 percent confidence intervals, and their correlation coefficients are compared with their counterparts determined from the more traditional stress versus strain form of the data. It was found that the use of the stress rate form of the data gave well determined parameter estimates with equivalent or superior fitting statistics to the same data can be used to extract significant and distinct sensitivity coefficients to enchance the overall robustness of an optimization algorithm for a parameter estimation.
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Research context
OpenAlex topics: Probabilistic and Robust Engineering Design · Structural Health Monitoring Techniques · Infrastructure Maintenance and Monitoring
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Affiliations
Sandia National Laboratories, Albuquerque, NM 87185
References (9)
Show source-provided references
1. Bard, Y., 1974, Nonlinear Parameter Estimation, Academic Press, New York.
2. Donaldson J. R. , and SchnabelR. B., 1987, “Computational Experience with Confidence Regions and Confidence Intervals for Nonlinear Least Squares,” Technometrics, Vol. 29, pp. 67–82.10.1080/00401706.1987.10488184 ↗
3. Fossum, A. F., 1997, “Parameter Estimation for an Internal Variable Model Using Nonlinear Optimization and Analytical/Numerical Response Sensitivities,” ASME JOURNAL OF ENGINEERING MATERIALS AND TECHNOLOGY, Vol.119, (To appear).10.1115/1.2812267 ↗
4. Freed, A. D., 1997 Visco Solids (in preparation at NASA Lewis Resrach Center, Cleveland, OH).
5. Harter H. L. , 1964, “A New Table of percentage Points of the Chi-Square Distribution,” Biometrika, Vol. 51, pp. 231–239.10.1093/biomet/51.1-2.231 ↗
6. Neilsen, M. K., S. N. Burchett, C. M. Stone, and J. J. Stephens, 1996, “A Viscoplastic Theory for Braze Alloys,” Sandia Report, SAND96-0984, Sandia National Laboratories, Albuquerque, NM 87185.10.2172/233338 ↗
7. Senseny P. E. , and FossumA. F., 1995, “On Testing Requirements for Viscoplastic Constitutive Parameter Estimation,”ASME JOURNAL OF ENGINEERING MATERIALS AND TECHNOLOGY, Vol.117, pp. 151–156.10.1115/1.2804522 ↗
8. Snedecor, G. W., and Cochran, W. G., 1980, Statistical Methods, The Iowa State University Press, Ames, Iowa.
9. Witkowski W. R. , and AllenJ. J., 1993, “Approximation of Parameter Uncertainty in Nonlinear Optimization Based Parameter Estimation Schemes,” AIAA Journal, Vol. 31, pp. 947–950.10.2514/3.11709 ↗