mCRE-based parameter identification from full-field measurements: Consistent framework, integrated version, and extension to nonlinear material behaviors

In this paper, we address the effective and robust identification of material behavior parameters from full-field measurements obtained by means of the advanced Digital Image Correlation (DIC) experimental technique. The objective is to optimize the identification procedure by defining an appropriat...

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Bibliographic Details
Published in:Computer methods in applied mechanics and engineering 2022-10, Vol.400, p.115461, Article 115461
Main Authors: Nguyen, Hai Nam, Chamoin, Ludovic, Ha Minh, Cuong
Format: Article
Language:English
Subjects:
Constitutive models
Constitutive relation error
Constitutive relationships
Cost function
Data assimilation
Digital image correlation
Digital imaging
Engineering Sciences
Errors
Full-field measurements
Inverse problems
Mathematical models
Methodology
Noise measurement
Parameter identification
Robustness (mathematics)
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Summary:In this paper, we address the effective and robust identification of material behavior parameters from full-field measurements obtained by means of the advanced Digital Image Correlation (DIC) experimental technique. The objective is to optimize the identification procedure by defining an appropriate and flexible numerical methodology that automatically incorporates the limited knowledge on both the used mathematical model (which is always biased, whatever its complexity) and experimental data (which are numerous in the case of DIC, but inevitably noisy). The inverse methodology we propose, denoted DIC-mCRE, is based on the modified Constitutive Relation Error (mCRE) concept which is a convenient tool to deal with reliability of information. In this framework, the designed identification tool is constructed from a hybrid mathematical formulation with a cost function made of weighted modeling and observation error terms. The associated metric thus naturally considers and connects all error and uncertainty sources. We introduce here a consistent setting of the weighting factors with respect to measurement noise, that gives full sense to the quantification of the model quality. Additionally, an integrated version (called mI-DIC) of the methodology is developed, and an extension to nonlinear constitutive models is proposed together with a dedicated solver. The performance of the approach is analyzed and validated on several numerical experiments dealing with linear elasticity or nonlinear models, and using synthetic or real full-field data. •Reliability of information is consistently introduced in the inversion process.•Admissibility spaces and weights are defined with respect to uncertainty sources.•An integrated version of the mCRE-based inversion technique is introduced.•An extension to nonlinear constitutive models is proposed, with dedicated algorithm.
ISSN:0045-7825
1879-2138
DOI:10.1016/j.cma.2022.115461