Analysis of Fatigue Indicator Parameters for Ti-6Al-4V microstructures using extreme value statistics in the transition fatigue regime

Muth, Adrienne; John, Reji; Pilchak, Adam L.; Kalidindi, Surya R.

doi:10.1016/j.ijfatigue.2021.106441

Cited by 13 publications

(2 citation statements)

References 48 publications

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“…The statistical notion of fatigue life has been pointed repeatedly in the literature [79,80], where Gumbel [81,82], Weibull [83,84], and (log-)normal distributions [83] are often used to model the extreme value statistics. Utilizing CPFEM to investigate microstructure-sensitivity fatigue has been studied in the last decade or so and is still very much an active field of research, notably by David McDowell and collaborators [85,86,87,88,89,90,91,92].…”

Section: Discussionmentioning

confidence: 99%

Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods

Tran¹,

Robbe²,

Lim³

2023

Preprint

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Quantifying uncertainty associated with the microstructure variation of a material can be a computationally daunting task, especially when dealing with advanced constitutive models and fine mesh resolutions in the crystal plasticity finite element method (CPFEM). Numerous studies have been conducted regarding the sensitivity of material properties and performance to the mesh resolution and choice of constitutive model. However, a unified approach that accounts for various fidelity parameters, such as mesh resolutions, integration time-steps, and constitutive models simultaneously is currently lacking. This paper proposes a novel uncertainty quantification (UQ) approach for computing the properties and performance of homogenized materials using CPFEM, that exploits a hierarchy of approximations with different levels of fidelity. In particular, we illustrate how multi-level sampling methods, such as multi-level Monte Carlo (MLMC) and multi-index Monte Carlo (MIMC), can be applied to assess the impact of variations in the microstructure of polycrystalline materials on the predictions of homogenized materials properties. We show that by adaptively exploiting the fidelity hierarchy, we can significantly reduce the number of microstructures required to reach a certain prescribed accuracy. Finally, we show how our approach can be extended to a multi-fidelity framework, where we allow the underlying constitutive model to be chosen from either a phenomenological plasticity model or a dislocation-density-based model.

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Section: Discussionmentioning

confidence: 99%

Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods

Tran¹,

Robbe²,

Lim³

2023

Preprint

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“…These FIP values can be used to design materials with microstructural features less prone to fatigue failure. 1,4,5,6,7 . However, the nonlocal nature of FIPs introduces another variables that must be determined for accurate predictions.…”

Section: Introduction 11 Overviewmentioning

confidence: 99%

A Nonconformal Nonlocal Approach to Calculating Statistical Spread in Fatigue Indicator Parameters for Polycrystals

Moore

Martinez

2023

Preprint

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In the study of fatigue fracture in metals, fatigue indicator parameters (FIPs) are nonlocal quantities that are used to model and predict the driving force needed to incubate fatigue cracks. These FIP values can be used to design materials with microstructural features less prone to fatigue failure. However, the nonlocal nature of fatigue indicator parameters introduces another unknown variable that must be determined for accurate predictions: the volume over which nonlocal averages are performed. Many studies use nonlocal volumes that enclose a predetermined number of finite elements in a polygranular crystal plasticity simulation. To encapsulate the entire microstructure, these nonlocal volumes must be conformal to the microstructure (i.e., they do not overlap or have gaps between them). Some studies base the length scale of these nonlocal volumes on constant values or on the size of relevant microstructural features. It has been shown that if the length scale is too small, the nonlocal FIP predictions are mesh dependent. But, if the length scale is too large, the experimentally observed statistical spread in fatigue life is not captured. This work introduces a nonconformal nonlocal volume (i.e., a volume that surrounds each element and overlaps nonlocal volumes). Averaging FIP over this nonlocal volume both captures the spread in fatigue data and is mesh independent. It also allows for weighted nonlocal averages that would have excluded some of the microstructure using the conformal approach. While this approach is more accurate than the previous approaches, it does require a large amount of computational resources to determine each nonlocal volume, so a parallelized algorithm that is scalable across multiple computing nodes is employed. The example polycrystalline material for this work is Ti-6Al-4V, a common titanium alloy with a hexagonal closed-packed crystal structure.

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Modeling the statistical distribution of fatigue crack formation lifetime in large volumes of polycrystalline microstructures

Stopka

2023

Acta Materialia

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Analysis of Fatigue Indicator Parameters for Ti-6Al-4V microstructures using extreme value statistics in the transition fatigue regime

Cited by 13 publications

References 48 publications

Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods

Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods

A Nonconformal Nonlocal Approach to Calculating Statistical Spread in Fatigue Indicator Parameters for Polycrystals

Modeling the statistical distribution of fatigue crack formation lifetime in large volumes of polycrystalline microstructures

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