A probabilistic approach with built-in uncertainty quantification for the calibration of a superelastic constitutive model from full-field strain data

Paranjape, Harshad M.; Aycock, Kenneth I.; Bonsignore, Craig; Weaver, Jason D.; Craven, Brent A.; Duerig, T.W.

doi:10.1016/j.commatsci.2021.110357

Cited by 17 publications

(5 citation statements)

References 21 publications

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“…In this case the multi-fidelity surrogate was not used to infer the model parameters but to conduct a sensitivity analysis. Paranjape et al (2021) have trained surrogates of quantities of interest from finite element simulations samples made of superelastic Nickel-Titanium (NiTi) shape memory alloys. The quantities of interest were the global load and local deformation pattern of a loaded diamond-shaped sample, in which case the experimental observations resulted from a digital image correlation analysis.…”

Section: Bayesian Inferencementioning

confidence: 99%

Toward stochastic multiscale methods in continuum solid mechanics

Noels

2022

Advances in Applied Mechanics

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Section: Bayesian Inferencementioning

confidence: 99%

Toward stochastic multiscale methods in continuum solid mechanics

Noels

2022

Advances in Applied Mechanics

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“…Ring [241] Notched plate [238] [ 67, 107, 127, 145, 153, 173-175, 180, 190, 199, 205, 208, 209, 217, 222, 230, 234, 237, 238, 246-248, 251, 256, 262, 267, 274, 279, 282, 284, 292, 304, 305, 322] Meuwissen specimen [198,210,211] Diamond shape [287,303] Porous SMA [318,320] 3D printed [232,286,302,308]…”

Section: Conclusion and Suggestions For Future Workmentioning

confidence: 99%

Applying Full-Field Measurement Techniques for the Thermomechanical Characterization of Shape Memory Alloys: A Review and Classification

Delpueyo

Jury

Balandraud

et al. 2021

Shap. Mem. Superelasticity

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Full-field measurement techniques are now mature. As such, they have profoundly impacted the experimental mechanics community in recent years. The way that shape memory alloys (SMAs) are now tested is not spare from this deep-rooted trend, as reflected by the increasing number of papers published in the SMA literature. In this context, the aim of this contribution is to give an overview of the use of full-field measuring techniques for SMA characterization purposes. We recall first the basic principle of one volume and four surface techniques employed in this community. Several typical papers where such techniques are employed are then presented, by highlighting in each case the extent to which the thermomechanical response of SMAs could be better understood and modeled thanks to these techniques. A classification by several criteria of about 300 references is finally offered and discussed. The main criteria are the type of SMA, the type of technique employed to perform the measurements, and the type of test.

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“…Gang et al 26 and Ni et al 27 identified the viscoplastic model parameters and structure parameters with the aid of the Gaussian surrogate model, respectively. Machine learning is also employed as the surrogate model by Paranjape et al 28 Asaadi and Heyns, 15 Wu et al, 29 and Pyrialakos et al 30 also used artificial neural networks (NNs) to speed up the identification of material models. The artificial NN has been demonstrated to be an efficient, stable, and robust method for modeling complicated nonlinear problems in engineering 31,32 and is used in this paper to predict the likelihood function values corresponding to material parameters.…”

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%

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

Chen

Jin

et al. 2023

Fatigue Fract Eng Mat Struct

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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.

show abstract

A probabilistic approach with built-in uncertainty quantification for the calibration of a superelastic constitutive model from full-field strain data

Cited by 17 publications

References 21 publications

Toward stochastic multiscale methods in continuum solid mechanics

Toward stochastic multiscale methods in continuum solid mechanics

Applying Full-Field Measurement Techniques for the Thermomechanical Characterization of Shape Memory Alloys: A Review and Classification

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

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