Uncertainty Quantification in the Mechanical Response of Crystal Plasticity Simulations

Bandyopadhyay, Ritwik; Prithivirajan, Veerappan; Sangid, Michael D.

doi:10.1007/s11837-019-03551-3

Cited by 39 publications

(25 citation statements)

References 37 publications

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“…Detailed uncertainty quantification of the crystal plasticity parameters and their influence on the associated fatigue metrics (e.g. the SPSED) have been performed by the authors, as reported in [62]. The results indicate that the uncertainty in the crystal plasticity parameters may propagate to approximately 25% variability in the values of w p 10 (x * ) and w p 10 (x * ) [62].…”

Section: Discussionmentioning

confidence: 98%

“…the SPSED) have been performed by the authors, as reported in [62]. The results indicate that the uncertainty in the crystal plasticity parameters may propagate to approximately 25% variability in the values of w p 10 (x * ) and w p 10 (x * ) [62]. In the present context, it would imply that the calibrated distribution of W p critical might result in a slightly higher standard deviation, which would not affect the mean of W p critical and hence the predicted fatigue life.…”

Section: Discussionmentioning

confidence: 99%

“…After that, the microstructure is discretized using a finite element meshing software. The creation of a virtual three-dimensional polycrystalline microstructure is a multi-step iterative process [62]. First, microstructural attributes (grain size distribution, twin area fraction, etc.)…”

Section: (B) Generation Of Discretized Polycrystalline Microstructuresmentioning

confidence: 99%

“…With this, there are eight crystal plasticity parameters (γ 0 , n, g(0), H, H d , χ (0), A, A d ) which are to be calibrated. Starting with the crystal plasticity parameters reported for additively manufactured IN718 in [72], a Matlab-based genetic algorithm framework, as described in [62], is used to identify an optimal set of these parameters such that the homogenized macroscopic response of the model matches the experimentally obtained macroscopic stress-strain response of the material. During simulations, normal displacements on three mutually orthogonal adjacent surfaces of the SEMs are restricted, and normal displacement is specified on another face, as shown in figure 2b, to mimic the uniaxial applied loading condition.…”

Section: (B) Generation Of Discretized Polycrystalline Microstructuresmentioning

confidence: 99%

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Microstructure-sensitive critical plastic strain energy density criterion for fatigue life prediction across various loading regimes

et al. 2020

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In the present work, we postulate that a critical value of the stored plastic strain energy density (SPSED) is associated with fatigue failure in metals and is independent of the applied load. Unlike the classical approach of estimating the (homogenized) SPSED as the cumulative area enclosed within the macroscopic stress–strain hysteresis loops, we use crystal plasticity finite element simulations to compute the (local) SPSED at each material point within polycrystalline aggregates of a nickel-based superalloy. A Bayesian inference method is used to calibrate the critical SPSED, which is subsequently used to predict fatigue lives at nine different strain ranges, including strain ratios of 0.05 and −1, using nine statistically equivalent microstructures. For each strain range, the predicted lives from all simulated microstructures follow a lognormal distribution. Moreover, for a given strain ratio, the predicted scatter is seen to be increasing with decreasing strain amplitude; this is indicative of the scatter observed in the fatigue experiments. Finally, the lognormal mean lives at each strain range are in good agreement with the experimental evidence. Since the critical SPSED captures the experimental data with reasonable accuracy across various loading regimes, it is hypothesized to be a material property and sufficient to predict the fatigue life.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: (B) Generation Of Discretized Polycrystalline Microstructuresmentioning

confidence: 99%

Section: (B) Generation Of Discretized Polycrystalline Microstructuresmentioning

confidence: 99%

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Microstructure-sensitive critical plastic strain energy density criterion for fatigue life prediction across various loading regimes

et al. 2020

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“…A full uncertainty quantification analysis was performed on each parameter; the results are reported in Ref. 58.…”

Section: Computational Modelingmentioning

confidence: 99%

ICME Approach to Determining Critical Pore Size of IN718 Produced by Selective Laser Melting

Sangid

Ravi

Prithivirajan

et al. 2019

JOM

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A degree of porosity is expected in additively manufactured (AM) materials. To aid in the qualification of AM materials, the smallest pore size that results in a debit in the fatigue performance is quantified. In the work presented herein, crystal plasticity simulations are used to identify the stress concentration around pores of various sizes, revealing that a single 20-lm pore or two 10-lm pores (with centers spaced 15 lm apart) localize stress at the pore, as opposed to elsewhere in the microstructure. In situ microtomography and far-field high-energy x-ray diffraction microscopy were used to identify crack formation and the evolution of the grain-level micromechanical fields during cyclic loading. Eighteen cracks were observed (15 at pores, 3 at the surface) at highly stressed grains in a sample, although most did not propagate. The dominant crack was seen to originate from the free surface, which is rationalized by fracture mechanics.

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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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Uncertainty Quantification in the Mechanical Response of Crystal Plasticity Simulations

Cited by 39 publications

References 37 publications

Microstructure-sensitive critical plastic strain energy density criterion for fatigue life prediction across various loading regimes

Microstructure-sensitive critical plastic strain energy density criterion for fatigue life prediction across various loading regimes

ICME Approach to Determining Critical Pore Size of IN718 Produced by Selective Laser Melting

Generating polycrystalline microstructures with prescribed tensorial texture coefficients

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