Enhancing the information-richness of sheet metal specimens for inverse identification of plastic anisotropy through strain fields

Zhang, Yi; Gothivarekar, Sanjay; Conde, M.; Velde, A. Van de; Paermentier, Benoît; Andrade‐Campos, A.; Coppieters, Sam

doi:10.1016/j.ijmecsci.2021.106891

Cited by 16 publications

(9 citation statements)

References 30 publications

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“…Indeed, for a simple model such as the Hill48 model, it is clearly shown that the identified parameters largely depend on the adopted heterogeneous experiment. Similar conclusions were repeatedly reported [3,8,16], e.g.…”

Section: Table 5 Comparison Of Reference Parameters Values and Identi...supporting

confidence: 92%

“…Although it was previously shown a numerical study of Zhang et al [16] that Test C R45 exhibits a better identifiability than Test C TD, this could not be confirmed experimentally in this study due to the large material model error associated with the Hill48 yield criterion. Indeed, for a simple model such as the Hill48 model, it is clearly shown that the identified parameters largely depend on the adopted heterogeneous experiment.…”

Section: Table 5 Comparison Of Reference Parameters Values and Identi...contrasting

confidence: 65%

“…The heterogeneous specimen shape was designed using shape optimization driven by the maximization of the strain heterogeneity. More details can be found in [16]. It must be noted that slight geometrical deviations were observed between numerical specimen design and the manufactured specimen.…”

Section: Fea Modelmentioning

confidence: 99%

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Identification of Anisotropic Yield Functions Using FEMU and an Information-Rich Tensile Specimen

Zhang

Andrade‐Campos

Coppieters

2022

KEM

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To fully exploit the predictive accuracy of advanced anisotropic yield functions, a large number of classical mechanical tests is required for calibration purposes. The Finite Element Model Updating (FEMU) technique enables to simultaneously extract multiple anisotropic parameters when fed with heterogeneous strain fields obtained from a single information-rich experiment. This inverse approach has the potential to mitigate the experimental calibration effort by resorting to a single, yet more complex experiment augmented with Digital Image Correlation. In this paper, we inversely identify the sought anisotropic parameters of two selected yield functions for a low carbon steel sheet based on the previously designed information-rich tensile specimen. The experimentally acquired strain field data is used to inversely identify the Hill48 yield criterion and the Yld2000-2d yield function, respectively. The results are compared with conventional calibration methods for both anisotropic yield functions. The inverse identification is then thoroughly studied using virtual experiments enabling to disentangle the effect of the material model error and the strain reconstruction error (DIC), respectively. It is shown that the material model error dominates the inverse identification of the Hill48 yield criterion. The reduced material model error for the Yld2000-2d yield function enables obtain inversely identified anisotropic parameters that are closer to the reference parameters. The paper clearly shows the importance of the predictive accuracy of the selected anisotropic yield function when applying inverse identification. Keywords: Anisotropic yield criteria; Material parameters identification; Heterogeneous mechanical tests; Inverse identification; DIC.

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Section: Table 5 Comparison Of Reference Parameters Values and Identi...supporting

confidence: 92%

Section: Table 5 Comparison Of Reference Parameters Values and Identi...contrasting

confidence: 65%

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Identification of Anisotropic Yield Functions Using FEMU and an Information-Rich Tensile Specimen

Zhang

Andrade‐Campos

Coppieters

2022

KEM

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“…However, the stress/strain fields generated during these tests are mainly homogeneous, not conveniently representing the more complex stress/strain states that occur during the forming processes. To overcome this problem, several authors developed mechanical tests characterized by non-homogeneous stress and strain fields [3,4], such as the biaxial tensile test on a cruciform specimen.…”

Section: Introductionmentioning

confidence: 99%

Variance-Based Sensitivity Analysis of the Biaxial Test on a Cruciform Specimen

Pereira

Oliveira

Fernandes

et al. 2022

KEM

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Parameter identification is a key aspect in the modeling of the material’s mechanical behavior. The identification quality depends on the sensitivity of the test results to the values of the constitutive parameters. In this context, a variance-based sensitivity analysis is performed, in order to quantify the influence of the material parameters on the results of the biaxial test on a cruciform specimen; in particular the influence of the Swift hardening law parameters and the Hill'48 anisotropic yield criterion on the forces, the principal strains, strain path and thickness reduction. In general, it was concluded that the Swift hardening parameters influence the forces and the major principal strain value, while the anisotropy parameters have the most influence in the principal strains, strain path and thickness reduction along the surface of the cruciform specimen.

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“…Therefore, an innovative heterogeneous mechanical test for uniaxial loading conditions developed in [16] using an optimization approach based on a heterogeneity indicator is going to be used for the parameter identification of the Swift law. Although it seems unnecessary to use a heterogeneous test for the calibration of the Swift law, it was concluded otherwise with the methodology present in [17]. The influence of the parameters on the strain gradients was evaluated and it was demonstrated that a more heterogeneous test presents better identifiability of these parameters when compared to a test with reduced heterogeneity.…”

mentioning

confidence: 99%

Parameter Identification of Swift Law Using a FEMU-Based Approach and an Innovative Heterogeneous Mechanical Test

Coppieters

Henriques

Andrade‐Campos

2022

KEM

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The reliability and predictive accuracy of forming simulation depend on both the material constitutive model and its inherent parameters. As opposed to conventional sheet metal material testing, heterogeneous mechanical tests provide more complex strain and stress states. Heterogeneous mechanical tests can be used to efficiently predict the material behavior in forming processes due to an improvement in the time required and accuracy in the identification of the parameters. The present work aims at identifying the Swift hardening law parameters of a dual-phase steel by means of an optimum-designed interior notched specimen that presents several strain and stress states simultaneously. The finite element model updating (FEMU) technique was used for the identification of parameters, by comparing a DIC-measured virtual material with numerical results iteratively DIC-filtered.

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Enhancing the information-richness of sheet metal specimens for inverse identification of plastic anisotropy through strain fields

Cited by 16 publications

References 30 publications

Identification of Anisotropic Yield Functions Using FEMU and an Information-Rich Tensile Specimen

Identification of Anisotropic Yield Functions Using FEMU and an Information-Rich Tensile Specimen

Variance-Based Sensitivity Analysis of the Biaxial Test on a Cruciform Specimen

Parameter Identification of Swift Law Using a FEMU-Based Approach and an Innovative Heterogeneous Mechanical Test

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