Robust Optimization Scheme for Inverse Method for Crystal Plasticity Model Parametrization

Shahmardani, Mahdieh; Vajragupta, Napat; Hartmaier, Alexander

doi:10.3390/ma13030735

Cited by 13 publications

(5 citation statements)

References 67 publications

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“…This work was dedicated to identifying constitutive parameters for a small strain crystal plasticity constitutive law incorporating a kinematic hardening model to capture the behavior under cyclic loading conditions, a topic which received attention recently from, both, a scientific and applied perspective (Sedighiani et al 2020;Shahmardani et al 2020;Shenoy et al 2008;Prithivirajan and Sangid 2018;Guery et al 2016;Hochhalter et al 2020;Kapoor et al 2021;Pagan et al 2017;Bandyopadhyay et al 2020;Rovinelli et al 2018;Bandyopadhyay et al 2019).…”

Section: Discussionmentioning

confidence: 99%

Identifying material parameters in crystal plasticity by Bayesian optimization

et al. 2021

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In this work, we advocate using Bayesian techniques for inversely identifying material parameters for multiscale crystal plasticity models. Multiscale approaches for modeling polycrystalline materials may significantly reduce the effort necessary for characterizing such material models experimentally, in particular when a large number of cycles is considered, as typical for fatigue applications. Even when appropriate microstructures and microscopic material models are identified, calibrating the individual parameters of the model to some experimental data is necessary for industrial use, and the task is formidable as even a single simulation run is time consuming (although less expensive than a corresponding experiment). For solving this problem, we investigate Gaussian process based Bayesian optimization, which iteratively builds up and improves a surrogate model of the objective function, at the same time accounting for uncertainties encountered during the optimization process. We describe the approach in detail, calibrating the material parameters of a high-strength steel as an application. We demonstrate that the proposed method improves upon comparable approaches based on an evolutionary algorithm and performing derivative-free methods.

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Section: Discussionmentioning

confidence: 99%

Identifying material parameters in crystal plasticity by Bayesian optimization

et al. 2021

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“…The material identification process is done using the trust-region-reflective algorithm for the minimization of the loss function that quantifies the difference between experimental and numerical results, where the latter are a function of the variable material parameters that are optimized iteratively. [39] For a detailed description of the identification procedure, the reader is referred to previous publications. [1,39] The identified material parameters using this optimization process are listed in Table III.…”

Section: Austenitic Steelmentioning

confidence: 99%

“…[39] For a detailed description of the identification procedure, the reader is referred to previous publications. [1,39] The identified material parameters using this optimization process are listed in Table III.…”

Section: Austenitic Steelmentioning

confidence: 99%

Microstructure-Sensitive Crystal Plasticity Modeling for Austenitic Steel and Nickel-Based Superalloy Under Isothermal Fatigue Loading

Shahmardani

Hartmaier

2023

Metall Mater Trans A

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Intermittent mechanical loads combined with high temperatures appear during the operation of turbines in jet engines or in power plants, which can lead to high-temperature fatigue or to thermomechanical fatigue. Since the assessment of fatigue properties is a complex and time-consuming process, it is essential to develop validated material models that are capable of predicting fatigue behavior, thus allowing the extrapolation of experimental results into a broader range of thermomechanical conditions. To accomplish this, two representative volume elements (RVEs), mimicking the typical microstructure of single crystal Ni-based superalloys and polycrystalline austenitic steels, respectively, are introduced. With the help of these RVEs, the temperature and deformation-dependent internal stresses in the microstructure can be taken into account. In the next step, phenomenological crystal plasticity models are implemented and parameterized for cyclic deformation of these two materials. The RVE, constitutive model, and the material parameters for the Ni-based superalloy are taken from a former study. For the austenitic steel, however, an inverse procedure has been used to identify its material parameters based on several isothermal fatigue tests in a wide temperature range. With the identified material parameters, a valid description of the isothermal fatigue behavior at different temperatures is possible. The most important conclusion from the comparison of the isothermal fatigue behavior of the two different materials is that the kinematic hardening, which is responsible for the shape of the hysteresis loops, is entirely described by the internal stresses within the typical microstructure of the Ni-based superalloy, which is modeled in a scale-bridging approach. Hence, no additional terms for kinematic hardening need to be introduced to describe the cyclic plasticity in the superalloy. For the austenitic steel, in contrast, the Ohno–Wang model for kinematic hardening needs to be considered additionally to the internal stresses in the polycrystalline microstructure to obtain a correct description of its cyclic plasticity.

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“…Researchers in [18] have evaluated the plasticity scheme, which is a model meant to define the constrututive connection of SMs. The model was effectively modified to signify super-elasticity and SMA effect of SMs.…”

Section: B Model Analysis Of Smsmentioning

confidence: 99%

An Detailed review on Applications of Smart Materials in Structural Engineering

2021

JMC

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Smart structural materials are a fundamental aspect that requires critical analysis in in structural engineering. This research contribution considers two vital crystal structures: Martensite and Austenite with their characteristic variations under different temperatures. From this research, it is seen the smart materials presents two vital features, which are distinguished from typical steels. The first one is the memory shape and the second one is super-elasticity. All of these properties can suit various applications in structural engineering e.g. dual actuators, self-rehabilitation and pre-stress bars. This research is purposed at analyzing the applications of smart materials in the field of structural engineering through the focus of relevant literature reviews, typical collection of data and major mechanic elements of smart materials. In axial tension evaluation, the curve for force and extension and the strain and stress curves of super-elasticity and Shape Memory Alloy (SMA) materials have been considered. The beam experiment with the superelasticity materials considered as the bars for reinforcement has been considered as well. This contribution provides the initial step of evaluation of the wide-range application of smart materials in the field of structural engineering.

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Robust Optimization Scheme for Inverse Method for Crystal Plasticity Model Parametrization

Cited by 13 publications

References 67 publications

Identifying material parameters in crystal plasticity by Bayesian optimization

Identifying material parameters in crystal plasticity by Bayesian optimization

Microstructure-Sensitive Crystal Plasticity Modeling for Austenitic Steel and Nickel-Based Superalloy Under Isothermal Fatigue Loading

An Detailed review on Applications of Smart Materials in Structural Engineering

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