On the tri-dimensional constitutive theory identification of linearly viscoelastic solids based on Bayesian framework

Yue, Lingyu; Heuzey, M. C.; Jalbert, Jonathan; Lévesque, Martin

doi:10.1016/j.ijsolstr.2021.111157

Cited by 4 publications

(5 citation statements)

References 39 publications

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“…Equation ( 19) was solved through a Bayesian approach, [51,52] which allows simultaneously identifying viscoelastic parameters and shift factors of PA6. The master curve and time-dependent Young's modulus were then constructed from the identification results.…”

Section: Viscoelastic Parameter Identification For Pa6 Resinmentioning

confidence: 99%

A dual‐scale three‐dimensional thermo‐viscoelastic model for the hot stamping simulation of thermoplastic composites

et al. 2022

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Existing hot stamping simulations are performed at the macroscopic scale, making optimizing process parameters for a new composite system time‐consuming. This paper develops a dual‐scale three‐dimensional thermos‐viscoelastic model based on a numerical homogenization approach to accurately predict the structural deformation of thermoplastic composite laminates during hot stamping. The viscoelastic homogenization model is developed on representative volume elements using temperature‐dependent viscoelastic parameters determined from thermo‐mechanical tests. The effective properties of the composites are identified from the homogenization results, which are then combined with the experimentally measured thermal behavior into a numerical model of the hot stamping process. The developed model is validated by the agreement between the finite element homogenization results and the predictions made using the determined parameters. The simulated warpage of the molded part also agrees well with the experimental measurements. The developed dual‐scale model can be used to predict the effective thermomechanical properties of composites. Moreover, it can also be integrated into numerical tools for structural design and process optimization of thermoplastic composites.

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Section: Viscoelastic Parameter Identification For Pa6 Resinmentioning

confidence: 99%

A dual‐scale three‐dimensional thermo‐viscoelastic model for the hot stamping simulation of thermoplastic composites

et al. 2022

Self Cite

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“…In addition, the parameters of the viscoelastic models are associated with considerable uncertainties, and the characterization of these parameters is a difficult task; thus, the Bayesian method is employed widely. [10][11][12][13][14] The Bayesian method is also applied to identify parameters of other nonlinear constitutive models, such as elastoplastic and viscoplastic models. Asaadi and Heyns 15 proposed a computational framework to identify the parameters of plasticity models by Bayes' theorem.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the corresponding mechanical tests are essential to accurately identify the model parameters by the Bayesian inference. 14,15,28 In this paper, a Bayesian parameter identification framework for the elastoplastic model including the Chaboche kinematic hardening and the Voce isotropic hardening rule is established. The strain-controlled low cycle fatigue (LCF) tests are first conducted to provide the real experimental data.…”

Section: Introductionmentioning

confidence: 99%

“…To avoid the problem of multiple solutions, Li et al 9 used the Bayesian method to calibrate the elastic modules of the concrete dam. In addition, the parameters of the viscoelastic models are associated with considerable uncertainties, and the characterization of these parameters is a difficult task; thus, the Bayesian method is employed widely 10–14 . The Bayesian method is also applied to identify parameters of other nonlinear constitutive models, such as elastoplastic and viscoplastic models.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, since the synthetic data are generated by the target mechanical model, the uncertainty of the model is also ignored. Therefore, the corresponding mechanical tests are essential to accurately identify the model parameters by the Bayesian inference 14,15,28 …”

Section: Introductionmentioning

confidence: 99%

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The constitutive model serves as the foundation for executing structure analysis to obtain the deformation and stress/strain. In this paper, a neural network‐assisted Bayesian parameter identification framework is presented to calibrate parameters of the constitutive model while considering the unavoidable uncertainties. The low‐cycle fatigue test of the CuCrZr alloy at 700 K is first performed to provide realistic data. The posterior distributions are obtained by applying the transitional Markov Chain Monte Carlo method. To accelerate the identification, the neural network is adopted to directly predict the likelihood function value given material parameters. The effect of prior distributions on the identification parameters is also studied. The characteristic parameters of the normal distribution have almost no effect on the identification results. In the absence of prior information, uniform prior distributions can be used to perform Bayesian identification of material parameters, and satisfactory identification parameters can also be acquired.

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On the parameters identification of three-dimensional aging-temperature-dependent viscoelastic solids through a Bayesian approach

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On the tri-dimensional constitutive theory identification of linearly viscoelastic solids based on Bayesian framework

Cited by 4 publications

References 39 publications

A dual‐scale three‐dimensional thermo‐viscoelastic model for the hot stamping simulation of thermoplastic composites

A dual‐scale three‐dimensional thermo‐viscoelastic model for the hot stamping simulation of thermoplastic composites

Parameter identification of elastoplastic model for CuCrZr alloy by the neural network‐aided Bayesian inference

On the parameters identification of three-dimensional aging-temperature-dependent viscoelastic solids through a Bayesian approach

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