A one step prediction error approach to the identification of viscoplastic material models
(2003) IUTAM Symposium on Field Analyses for Determination of Material Parameters - Experimental and Numerical Aspects In Solid Mechanics and its Applications 109. p.101-112- Abstract
- A new approach to the problem of estimating parameter in material models is presented. The approach is based on a state space representation of the constitutive equations and one step predictions. The differences between one-step predictions and the corresponding measurements are used to design generic one-step prediction error estimators, and in particular, the maximum likelihood method is presented. The one-step predictions are computed through extended Kalman filtering. Consequences of using a time dependent model with least squares regression are analysed. It is shown that if the residuals are a sequence of stochastic variables, correlated with the regressors, the parameter estimates may be biased. A Monte Carlo study shows that the... (More)
- A new approach to the problem of estimating parameter in material models is presented. The approach is based on a state space representation of the constitutive equations and one step predictions. The differences between one-step predictions and the corresponding measurements are used to design generic one-step prediction error estimators, and in particular, the maximum likelihood method is presented. The one-step predictions are computed through extended Kalman filtering. Consequences of using a time dependent model with least squares regression are analysed. It is shown that if the residuals are a sequence of stochastic variables, correlated with the regressors, the parameter estimates may be biased. A Monte Carlo study shows that the model parameters of a Norton viscoplastic model are estimated with up to 40% higher precision with the new approach as compared to standard least squares regression. An analysis of the residuals clearly shows that the residuals of the new estimators form an independent sequence of random variables. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1406962
- author
- Wall, Ola LU and Holst, Jan LU
- organization
- publishing date
- 2003
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- identification, material models, visco-plasticity
- host publication
- IUTAM Symposium on Field Analyses for Determination of Material Parameters - Experimental and Numerical Aspects
- series title
- Solid Mechanics and its Applications
- editor
- Ståhle, P. and Sundin, K. G.
- volume
- 109
- pages
- 101 - 112
- publisher
- Springer
- conference name
- IUTAM Symposium on Field Analyses for Determination of Material Parameters - Experimental and Numerical Aspects
- conference location
- Kiruna, Sweden
- conference dates
- 2000-07-31 - 2000-08-04
- external identifiers
-
- wos:000183488300010
- ISSN
- 0925-0042
- ISBN
- 978-1-4020-1283-9
- language
- English
- LU publication?
- yes
- id
- 36a682aa-9b74-42a5-b8dd-ce5f3e7a17ce (old id 1406962)
- date added to LUP
- 2016-04-04 10:17:20
- date last changed
- 2020-01-29 15:02:58
@inproceedings{36a682aa-9b74-42a5-b8dd-ce5f3e7a17ce, abstract = {{A new approach to the problem of estimating parameter in material models is presented. The approach is based on a state space representation of the constitutive equations and one step predictions. The differences between one-step predictions and the corresponding measurements are used to design generic one-step prediction error estimators, and in particular, the maximum likelihood method is presented. The one-step predictions are computed through extended Kalman filtering. Consequences of using a time dependent model with least squares regression are analysed. It is shown that if the residuals are a sequence of stochastic variables, correlated with the regressors, the parameter estimates may be biased. A Monte Carlo study shows that the model parameters of a Norton viscoplastic model are estimated with up to 40% higher precision with the new approach as compared to standard least squares regression. An analysis of the residuals clearly shows that the residuals of the new estimators form an independent sequence of random variables.}}, author = {{Wall, Ola and Holst, Jan}}, booktitle = {{IUTAM Symposium on Field Analyses for Determination of Material Parameters - Experimental and Numerical Aspects}}, editor = {{Ståhle, P. and Sundin, K. G.}}, isbn = {{978-1-4020-1283-9}}, issn = {{0925-0042}}, keywords = {{identification; material models; visco-plasticity}}, language = {{eng}}, pages = {{101--112}}, publisher = {{Springer}}, series = {{Solid Mechanics and its Applications}}, title = {{A one step prediction error approach to the identification of viscoplastic material models}}, volume = {{109}}, year = {{2003}}, }