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

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

doi:10.1016/j.ijfatigue.2020.106096

Cited by 16 publications

(6 citation statements)

References 65 publications

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“…Despite the fact that experimental results are direct manifestation of mechanical properties of materials, uncertainties and cost through the exhaustive experimental process cannot be neglected. In contrast, numerically quantifying microstructural attributes tends to be a promising alternative strategy to lower experimental cost and to tailor desirable microstructure [32][33][34][35]. Numerical investigations into LM are primarily carried out within the framework of crystal plasticity finite element method (CPFEM).…”

Section: Introductionmentioning

confidence: 99%

Quantifying lamellar microstructural effect on the fatigue performance of bimodal Ti-6Al-4V with microdefect

Tang

Chen²,

Ferro³

et al. 2022

International Journal of Fatigue

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Section: Introductionmentioning

confidence: 99%

Quantifying lamellar microstructural effect on the fatigue performance of bimodal Ti-6Al-4V with microdefect

Tang

Chen²,

Ferro³

et al. 2022

International Journal of Fatigue

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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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“…For this purpose, all FIPs computed from a set of SVEs of the statistically same structure, that are above a threshold value are fitted to a distribution function. Figure 7 illustrates this EVD approach as employed in [30,40,56], among others, at the example of SVE sets with differing porosity 𝜙. Here, the 99.999 percentile is chosen as threshold value as it was found to be optimal for the conducted investigation.…”

Section: Fatigue Indicating Parameters From Numerical Simulationsmentioning

confidence: 99%

“…The approach of predicting FIPs based on microstructure simulations is subject of many recent publications, for example, [30,33,40], and consists mainly of the following steps: (a) an appropriate structure characterization, (b) the generation of simulation domains, that is, structure reconstruction, (c) the constitutive modeling, that is, simulation and (d) subsequent statistical data processing and analysis, as outlined hereafter.…”

mentioning

confidence: 99%

Accessing pore microstructure–property relationships for additively manufactured materials

et al. 2021

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Understanding structure-property (SP) relationships is essential for accelerating materials innovation. Still being in the state of ongoing research and development, this is especially true for additive manufacturing (AM) in which process-induced imperfections like pores and microstructural variations significantly influence the material's properties. That is why, the present work aims at proposing an approach for accessing pore SP relationships for AM materials. For this purpose, crystal plasticity (CP) simulations on reconstructed domains based on experimental measurements are employed to allow for a microstructure-sensitive investigation. For the considered Ti-6Al-4V specimen manufactured by laser powder bed fusion, the microstructure and pore characteristics are obtained by utilizing light microscopy and X-ray computed tomography at the microscale. Employing suitable statistical analysis and reconstruction, statistical volume elements with reconstructed pore distributions are created. Using them, microscale CP simulations are performed to obtain fatigue indicating parameters. Employing a further statistical analysis, fatigue ranking parameters are derived for a comparison of different microstructures. Additionally, a comparison with the empirical Murakami's square root area concept is made. Results from first numerical studies underline the potential of the approach for understanding and improving AM materials.

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

Cited by 16 publications

References 65 publications

Quantifying lamellar microstructural effect on the fatigue performance of bimodal Ti-6Al-4V with microdefect

Quantifying lamellar microstructural effect on the fatigue performance of bimodal Ti-6Al-4V with microdefect

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

Accessing pore microstructure–property relationships for additively manufactured materials

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