Computational Micromechanics Modeling of Polycrystalline Superalloys: Application to Inconel 718

Cruzado, A.; Llorca, Javier; Escudero, Javier Segurado

doi:10.1007/978-3-030-40562-5_5

Cited by 5 publications

(5 citation statements)

References 66 publications

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“…involving the plastic slip rate γ α of slip system α. As we consider cyclic loading scenarios, we use a model for the plastic slip rate γα which captures typical cyclic phenomena, such as the Bauschinger effect [52] and ratcheting [53]. More precisely, for the study at hand, we use the flow rule proposed by Hutchinson [54]…”

Section: Crystal Plasticity Modelmentioning

confidence: 99%

Generating polycrystalline microstructures with prescribed tensorial texture coefficients

Kuhn

Schneider²,

Sonnweber-Ribic³

et al. 2022

Comput Mech

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This work is concerned with synthetic microstructure models of polycrystalline materials. Once a representation of the microstructure is generated, the individual grains need to be furnished with suitable crystal orientations, matching a specific crystal orientation distribution. We introduce a novel method for this task, which permits to prescribe the orientations based on tensorial Fourier coefficients. This compact representation gives rise to the texture coefficient optimization for prescribing orientations method, enabling the determination of representative orientations for digital polycrystalline microstructures. We compare the proposed method to established and dedicated algorithms in terms of the linear elastic as well as the non-linear plastic behavior of a polycrystalline material.

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Section: Crystal Plasticity Modelmentioning

confidence: 99%

Generating polycrystalline microstructures with prescribed tensorial texture coefficients

Kuhn

Schneider²,

Sonnweber-Ribic³

et al. 2022

Comput Mech

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“…Confinement of an RVE with a cuboidal shape might cause short edges on the exterior edges. Irregularly shaped but geometrically periodic RVEs [21][22][23] are generated using Voro++ software library [24]. Fig.…”

Section: Computational Homogenizationmentioning

confidence: 99%

Response of 2d and 3d Crystal Plasticity Models Subjected to Plane Strain Condition

Mirhosseini

Perdahcıoğlu

Atzema

et al. 2022

Preprint

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“…decrease for increasing number of cycles, see Farooq et al (2020) and Cruzado et al (2020) for recent discussions of ratcheting and the Bauschinger effect, respectively.…”

Section: Considered Crystal Plasticity Modelsmentioning

confidence: 99%

“…For modeling the response to cyclic loading accurately, the Bauschinger effect, which concerns a decrease of the resistance to plastic deformation also when a change in loading directions, for instance from tension to compression, takes place, needs to be accounted for Cruzado et al (2020). Also, the ratcheting behavior, i.e., the consistent accumulation of plastic slip under stress-driven cyclic loading conditions, is necessary to consider (Farooq et al 2020).…”

Section: Introductionmentioning

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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Computational Micromechanics Modeling of Polycrystalline Superalloys: Application to Inconel 718

Cited by 5 publications

References 66 publications

Generating polycrystalline microstructures with prescribed tensorial texture coefficients

Generating polycrystalline microstructures with prescribed tensorial texture coefficients

Response of 2d and 3d Crystal Plasticity Models Subjected to Plane Strain Condition

Identifying material parameters in crystal plasticity by Bayesian optimization

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