Improving full-field identification using progressive model enrichments

Abstract Full-field identification methods such as finite element model updating or integrated digital image correlation minimize the gap between an experiment and a simulation by iterative schemes. Within the algorithms residual fields and sensitivity fields are used to achieve identification. This paper discusses how these same fields can be used to assess the quality of the identification and guide toward successive enrichment of the constitutive model to progressively reduce the experiment-model gap. A cyclic experiment on a dog-bone sample made of aluminum alloy is used as an example to identify the parameters of an elastoplastic model with exponential hardening and anisotropic yielding.

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