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Article

A Methodology for the Statistical Calibration of Complex Constitutive Material Models: Application to Temperature-Dependent Elasto-Visco-Plastic Materials

1
IMDEA Materials Institute, Eric Kandel, 2, 28906 Getafe, Spain
2
Mechanical Eng. Department, Universidad Politécnica de Madrid, José Gutiérrez Abascal, 2, 28006 Madrid, Spain
3
AIRBUS Operations S.L., John Lennon S/N, 28906 Getafe, Spain
*
Author to whom correspondence should be addressed.
Materials 2020, 13(19), 4402; https://doi.org/10.3390/ma13194402
Received: 18 August 2020 / Revised: 22 September 2020 / Accepted: 28 September 2020 / Published: 2 October 2020
(This article belongs to the Special Issue Empowering Materials Processing and Performance from Data and AI)
The calibration of any sophisticated model, and in particular a constitutive relation, is a complex problem that has a direct impact in the cost of generating experimental data and the accuracy of its prediction capacity. In this work, we address this common situation using a two-stage procedure. In order to evaluate the sensitivity of the model to its parameters, the first step in our approach consists of formulating a meta-model and employing it to identify the most relevant parameters. In the second step, a Bayesian calibration is performed on the most influential parameters of the model in order to obtain an optimal mean value and its associated uncertainty. We claim that this strategy is very efficient for a wide range of applications and can guide the design of experiments, thus reducing test campaigns and computational costs. Moreover, the use of Gaussian processes together with Bayesian calibration effectively combines the information coming from experiments and numerical simulations. The framework described is applied to the calibration of three widely employed material constitutive relations for metals under high strain rates and temperatures, namely, the Johnson–Cook, Zerilli–Armstrong, and Arrhenius models. View Full-Text
Keywords: model calibration; sensitivity analysis; elasto-visco-plasticity; Gaussian process model calibration; sensitivity analysis; elasto-visco-plasticity; Gaussian process
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MDPI and ACS Style

de Pablos, J.L.; Menga, E.; Romero, I. A Methodology for the Statistical Calibration of Complex Constitutive Material Models: Application to Temperature-Dependent Elasto-Visco-Plastic Materials. Materials 2020, 13, 4402. https://doi.org/10.3390/ma13194402

AMA Style

de Pablos JL, Menga E, Romero I. A Methodology for the Statistical Calibration of Complex Constitutive Material Models: Application to Temperature-Dependent Elasto-Visco-Plastic Materials. Materials. 2020; 13(19):4402. https://doi.org/10.3390/ma13194402

Chicago/Turabian Style

de Pablos, Juan L.; Menga, Edoardo; Romero, Ignacio. 2020. "A Methodology for the Statistical Calibration of Complex Constitutive Material Models: Application to Temperature-Dependent Elasto-Visco-Plastic Materials" Materials 13, no. 19: 4402. https://doi.org/10.3390/ma13194402

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