Parameter Identification from Mechanical Field Measurements using Finite Element Model Updating Strategies

Pagnacco, Emmanuel; Caro-Bretelle, A. S.; Ienny, Patrick

doi:10.1002/9781118578469.ch9

Cited by 13 publications

(16 citation statements)

References 40 publications

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“…It is referred to as loadbased finite element model updating (or FEMU-F [77,78]). As for displacement-based approaches, the load sensitivity matrix [S F (t)] to the sought parameters is computed to update {δp} from the current estimate {F FE ({ p})} of the reaction forces.…”

Section: D Mechanical Correlationmentioning

confidence: 99%

Toward 4D mechanical correlation

Hild

Bouterf

Chamoin

et al. 2016

Adv. Model. and Simul. in Eng. Sci.

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Background: The goal of the present study is to illustrate the full integration of sensor and imaging data into numerical procedures for the purpose of identification of constitutive laws and their validation. The feasibility of such approaches is proven in the context of in situ tests monitored by tomography. The bridging tool consists of spatiotemporal (i.e., 4D) analyses with dedicated (integrated) correlation algorithms. Methods: A tensile test on nodular graphite cast iron sample is performed within a lab tomograph. The reconstructed volumes are registered via integrated digital volume correlation (DVC) that incorporates a finite element modeling of the test, thereby performing a mechanical integration in 4D registration of a series of 3D images. In the present case a non-intrusive procedure is developed in which the 4D sensitivity fields are obtained with a commercial finite element code, allowing for a large versatility in meshing and incorporation of complex constitutive laws. Convergence studies can thus be performed in which the quality of the discretization is controlled both for the simulation and the registration. Results: Incremental DVC analyses are carried out with the scans acquired during the in situ mechanical test. For DVC, the mesh size results from a compromise between measurement uncertainties and its spatial resolution. Conversely, a numerically good mesh may reveal too fine for the considered material microstructure. With the integrated framework proposed herein, 4D registrations can be performed and missing boundary conditions of the reference state as well as mechanical parameters of an elastoplastic constitutive law are determined in fair condition both for DVC and simulation.

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Section: D Mechanical Correlationmentioning

confidence: 99%

Toward 4D mechanical correlation

Hild

Bouterf

Chamoin

et al. 2016

Adv. Model. and Simul. in Eng. Sci.

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show abstract

“…However, in contrast with usual applications of FEMU [6], boundary conditions in the depth are known to be approximate at best. This implies that the displacement on the observation surface cannot be securely compared with the computed values.…”

Section: Choice Of Metric For Umentioning

confidence: 99%

“…Finite Element Model Updating (i.e., FEMU [6]), which is described in Figure 2, will be used to calibrate material parameters. The FEMU algorithm is based on measured and computed displacement fields u, and applied load F data.…”

Section: Calibration Of Materials Parametersmentioning

confidence: 99%

Backtracking Depth-Resolved Microstructures for Crystal Plasticity Identification—Part 2: Identification

Shi¹,

Latourte²,

Hild³

et al. 2017

JOM

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The present study considers the identification of crystal plasticity parameters from the knowledge of a deformed microstructure, which has been backtracked to the reference state, and known kinematics along the sample surface. This theoretical question is tackled on a numerical (synthetic) test case. A 2D microstructure with one dimension along the depth is generated, and deformed using known crystal plasticity law. A procedure is proposed to perform the calibration of the constitutive parameters addressing the specific challenge of having a partial knowledge of the boundary conditions. The proposed identification strategy combined with the estimation of the reference microstructure is shown to retrieve the constitutive parameters with good accuracy.

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“…The full-field identification methods typically rely on sensitivity fields as part 40 of the tangent operator in the iterative optimization algorithm [22,23,24]. As the name suggests they indicate the sensitivity of a certain parameter on space and time data of interest.…”

Section: The Most Common Identification Methods Is Referred To As Finimentioning

confidence: 99%

“…In this method the distance between simulated and measured quantities, such as displacement [24] and/or force [10,24], are minimized by iteratively updating 75 the finite element model parameters. The key difference between FEMU and I-DIC is that FEMU minimizes the gap based on a measured displacement field and I-DIC minimizes the image residual directly using the model to drive the kinematics.…”

Section: Identification Frameworkmentioning

confidence: 99%

Improving full-field identification using progressive model enrichments

Neggers

Mathieu

Hild

et al. 2017

International Journal of Solids and Structures

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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 elasto-plastic model with exponential hardening and anisotropic yielding.

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Parameter Identification from Mechanical Field Measurements using Finite Element Model Updating Strategies

Cited by 13 publications

References 40 publications

Toward 4D mechanical correlation

Toward 4D mechanical correlation

Backtracking Depth-Resolved Microstructures for Crystal Plasticity Identification—Part 2: Identification

Improving full-field identification using progressive model enrichments

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