Crystal Plasticity Modeling of Laser Peening Effects on Tensile and High Cycle Fatigue Properties of 2024-T351 Aluminum Alloy

Toursangsaraki, Maziar; Wang, Huamiao; Hu, Yongxiang; Karthik, Durai

doi:10.1115/1.4050308

Cited by 13 publications

(2 citation statements)

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“…In another work, Toursangsaraki et al . also used the phenomenological CP model for investigating the laser peening effect on high cycle fatigue and tensile deformation of AA2024-T351 alloy (Toursangsaraki et al. , 2021).…”

Section: Introductionmentioning

confidence: 99%

“…However, periodic boundary conditions were not accounted for in these studies. In another work, Toursangsaraki et al also used the phenomenological CP model for investigating the laser peening effect on high cycle fatigue and tensile deformation of AA2024-T351 alloy (Toursangsaraki et al, 2021). Aghabalaeivahid et al modeled the effect of phases present in the AA2024 alloy on its macroscopic stress-strain response for uniaxial tension (Aghabalaeivahid and Shalvandi, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Computational homogenization based crystal plasticity investigation of deformation behavior of AA2024-T3 alloy at different strain rates

Singh

Vohra

et al. 2023

MMMS

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PurposeModeling of material behavior by physically or microstructure-based models helps in understanding the relationships between its properties and microstructure. However, the majority of the numerical investigations on the prediction of the deformation behavior of AA2024 alloy are limited to the use of phenomenological or empirical constitutive models, which fail to take into account the actual microscopic-level mechanisms (i.e. crystallographic slip) causing plastic deformation. In order to achieve accurate predictions, the microstructure-based constitutive models involving the underlying physical deformation mechanisms are more reliable. Therefore, the aim of this work is to predict the mechanical response of AA2024-T3 alloy subjected to uniaxial tension at different strain rates, using a dislocation density-based crystal plasticity model in conjunction with computational homogenization.Design/methodology/approachA dislocation density-based crystal plasticity (CP) model along with computational homogenization is presented here for predicting the mechanical behavior of aluminium alloy AA2024-T3 under uniaxial tension at different strain rates. A representative volume element (RVE) containing 400 grains subjected to periodic boundary conditions has been used for simulations. The effect of mesh discretization on the mechanical response is investigated by considering different meshing resolutions for the RVE. Material parameters of the CP model have been calibrated by fitting the experimental data. Along with the CP model, Johnson–Cook (JC) model is also used for examining the stress-strain behavior of the alloy at various strain rates. Validation of the predictions of CP and JC models is done with the experimental results where the CP model has more accurately captured the deformation behavior of the aluminium alloy.FindingsThe CP model is able to predict the mechanical response of AA2024-T3 alloy over a wide range of strain rates with a single set of material parameters. Furthermore, it is observed that the inhomogeneity in stress-strain fields at the grain level is linked to both the orientation of the grains as well as their interactions with one another. The flow and hardening rule parameters influencing the stress-strain curve and capturing the strain rate dependency are also identified.Originality/valueComputational homogenization-based CP modeling and simulation of deformation behavior of polycrystalline alloy AA2024-T3 alloy at various strain rates is not available in the literature. Therefore, the present computational homogenization-based CP model can be used for predicting the deformation behavior of AA2024-T3 alloy more accurately at both micro and macro scales, under different strain rates.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%