Uncertainty Quantification for Parameter Estimation and Response Prediction

Ricciardi, Denielle E.; Chkrebtii, Oksana A.; Niezgoda, Stephen R.

doi:10.1007/s40192-019-00154-3

Cited by 13 publications

(3 citation statements)

References 72 publications

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“…We presented the uncertainty in the calibrated material parameters in terms of the credibility interval. The credibility intervals may be reported in various ways including the highest posterior density interval (HPI) or equal-tailed interval [8]. One of such representations that is the most informative may be chosen when reporting the uncertainty.…”

Section: Advantages and Challenges Of The Bi Calibration Approachmentioning

confidence: 99%

“…Castillo and Kalidindi reported a calibration method based on BI to determine the single crystal elastic constants of metallic materials using indentation load-displacement data [7]. Ricciardi et al demonstrated the calibration of a crystal plasticity model using a BI approach [8]. Two efforts in the literature have specifically applied BI to the calibration of NiTi SMA constitutive model properties.…”

Section: Introductionmentioning

confidence: 99%

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A Probabilistic Approach with Built-in Uncertainty Quantification for the Calibration of a Superelastic Constitutive Model from Full-field Strain Data

Paranjape¹,

Aycock²,

Bonsignore³

et al. 2019

Preprint

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Superelastic Nitinol is a widely used material to manufacture implantable medical devices. Simulations are extensively used in the design, optimization, and durability assessment of Nitinol implants. The constitutive response of superelastic materials is non-linear, anisotropic, and asymmetric in tension vs. compression. The existing methods to determine the superelastic material properties used in finite element simulations typically do not account for some of these complexities. The goal of this work is to introduce a method to determine the superelastic material constitutive properties using full-field surface strain measurements. We propose using digital image correlation to obtain surface strain fields during tensile testing of specially designed diamond specimens. The material properties are determined by minimizing a difference metric defined in terms of the local strain difference and the global load difference between the experiment and trial simulations. We also propose a metric in terms of the local strain difference between experimental measurement and simulation prediction to report the credibility of the material properties fitted using this method. The philosophy behind this method and the proposed metric to quantify the accuracy of material property fitting reflects the growing consensus in the implantable device community that the simulation results must be accompanied by a quantification of their credibility.

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Section: Advantages and Challenges Of The Bi Calibration Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Probabilistic Approach with Built-in Uncertainty Quantification for the Calibration of a Superelastic Constitutive Model from Full-field Strain Data

Paranjape¹,

Aycock²,

Bonsignore³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, the Gaussian process emulator with Bayesian inference has been used to calibrate and quantify uncertainties in thermal conduction constitutive law from molecular dynamics simulations [36]. Recently, a Bayesian hierarchical model accounting for parameter uncertainty, material property variability and measurement noise has been proposed to calibrate Voce hardening parameters of a visco-plastic self-consistent crystal plasticity model and to propagate the uncertainty to stress-strain response [34,35]. An in-depth review of different techniques for quantifying and propagating uncertainties in multi-scale models is given in [16].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantified Parametrically Homogenized Constitutive Models for Microstructure-Integrated Structural Simulations

Kotha

Ozturk

Smarslok

et al. 2020

Integr Mater Manuf Innov

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This paper investigates the role of material microstructures in structural analysis and establishes the need for microstructureintegrated constitutive models in predicting structural response. A focus is on microstructure and temperature dependency of stresses and plastic strains in a structural panel under realistic loading conditions. Structural analysis is conducted using the recently developed uncertainty-quantified parametrically homogenized constitutive model (UQ-PHCM) for near-alpha Titanium alloys like Ti6242S. PHCMs exhibit explicit microstructural dependency and are developed from homogenization of crystal plasticity finite element simulation results with machine learning. Uncertainties due to model reduction, data sparsity and microstructural variability are accounted for in the model. Structural response with the UQ-PHCMs is compared to those predicted by isotropic elasticity and J 2 plasticity models without explicit microstructure information. Parametric studies illustrate how different uncertainties in the UQ-PHCM framework propagate to the structural response variables. The results also show the relative contribution of different microstructural features to the propagated uncertainty in structural response variables. The studies establish the UQ-PHCM as an effective tool for reliable structural analysis with consequences in material design.

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Best Practices in Developing a Workflow for Uncertainty Quantification for Modeling the Biodegradation of Mg‐Based Implants

AlBaraghtheh,

Willumeit‐Römer,

Zeller‐Plumhoff

2024

Advanced Science

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Computational models of electrochemical biodegradation of magnesium (Mg)‐based implants are uncertain. To quantify the model uncertainty, iterative evaluations are needed. This presents a challenge, especially for complex, multiscale models as is the case here. Approximating high‐cost and complex models with easy‐to‐evaluate surrogate models can reduce the computational burden. However, the application of this approach to complex degradation models remains limited and understudied. This work provides a workflow to quantify different types of uncertainty within biodegradation models. Three surrogate models—Kriging, polynomial chaos expansion, and polynomial chaos Kriging—are compared based on the minimum number of samples required for surrogate model construction, surrogate model accuracy, and computational time. The surrogate models are tested for three computational models representing Mg‐based implant biodegradation. Global sensitivity analysis and uncertainty propagation are used to analyze the uncertainties associated with the different models. The findings indicate that Kriging proves effective for calibrating diverse computational models with minimal computational time and cost, while polynomial chaos expansion and polynomial chaos Kriging exhibit greater capability in predicting propagated uncertainties within the computational models.

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Uncertainty Quantification for Parameter Estimation and Response Prediction

Cited by 13 publications

References 72 publications

A Probabilistic Approach with Built-in Uncertainty Quantification for the Calibration of a Superelastic Constitutive Model from Full-field Strain Data

A Probabilistic Approach with Built-in Uncertainty Quantification for the Calibration of a Superelastic Constitutive Model from Full-field Strain Data

Uncertainty Quantified Parametrically Homogenized Constitutive Models for Microstructure-Integrated Structural Simulations

Best Practices in Developing a Workflow for Uncertainty Quantification for Modeling the Biodegradation of Mg‐Based Implants

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