In this research, microstructure-based modeling is conducted to predict the fatigue crack nucleation life of a nickel-based alloy, Haynes 282, at different strain amplitudes from high cycle fatigue (HCF) to low cycle ...
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In this research, microstructure-based modeling is conducted to predict the fatigue crack nucleation life of a nickel-based alloy, Haynes 282, at different strain amplitudes from high cycle fatigue (HCF) to low cycle fatigue (LCF). A three-dimensional (3D) polycrystalline aggregate is constructed as the material representative volume element (RVE) using Voronoi tessellation with grain orientations assigned by random functions. The Hill's yield criteria and linear strain hardening are employed to investigate the anisotropic plastic deformation in each grain using the finite element method (FEM), with the associated parameters determined by matching the monotonic stress-strain relationship and cyclic hysteresis loops of Haynes 282 alloy on the macroscopic scale. The fatigue crack nucleation life of Haynes 282 alloy is predicted using the Tanaka-Mura-Wu (TMW) model based on the material surface energy, shear modulus, Burgers vector and the plastic strain range at the microstructural level. It is demonstrated that this approach is able to predict the fatigue crack nucleation life of Haynes 282 alloy and estimate the scattering of the fatigue life by numerical simulations with different sets of grain orientation distribution functions. The results of the model prediction are in good agreement with the experimental observations. Furthermore, the effect of grain orientation on fatigue crack nucleation is discussed.
The fracture processes in cement paste at microscale are simulated by the 3D lattice fracture model based on the microstructure of hydrating cement paste. The uniaxial tensile test simulation is carried out to obtain ...
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ISBN:
(纸本)9780878492411
The fracture processes in cement paste at microscale are simulated by the 3D lattice fracture model based on the microstructure of hydrating cement paste. The uniaxial tensile test simulation is carried out to obtain the load-displacement diagram and microcracks propagation for a Portland cement paste specimen in the size of 100x100x100 mu m(3) at the degree of hydration 69%. The Young's modulus, tensile strength, strain at peak load and fracture energy are computed on the basis of the load-displacement diagram.
Porous brittle solids evidence complex mechanical behavior, where localized failure patterns originate from mechanical processes on the microstructural level. In order to investigate the failure mechanics of porous br...
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Porous brittle solids evidence complex mechanical behavior, where localized failure patterns originate from mechanical processes on the microstructural level. In order to investigate the failure mechanics of porous brittle solids, we outline a general stochastic and numerical microstructure-based approach. To this end, we generate random porous microstructures by level-cutting Gaussian random fields, and conduct numerical simulations using the material point method. This allows investigating both small and large deformation characteristics of irregular porous media where a segmentation into grains and bonds is ambiguous. We demonstrate the versatility of our approach by examining elasticity and failure as a function of a wide range of porosities, from 20% to 80%. Observing that onset of failure can be well described through the second order work, we show that the stress at failure follows a power law similar to that of the elastic modulus. Moreover, we propose that the failure envelope can be approximated by a simple quadratic fitting curve, and that plastic deformation appears to be governed by an associative plastic flow rule. Finally, large deformation simulations reveal a transition in the mode of localization of the deformation, from compaction bands for highly porous samples to shear bands for denser ones.
Data-driven approaches enable a deep understanding of microstructure and mechanical properties of materials and greatly promote one's capability in designing new advanced materials. Deep learning-based image proce...
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Data-driven approaches enable a deep understanding of microstructure and mechanical properties of materials and greatly promote one's capability in designing new advanced materials. Deep learning-based image process-ing outperforms conventional image processing techniques with unsupervised learning. This study employs a variational autoencoder (VAE) to generate a continuous microstructure space based on synthetic microstructural images. The structure-property relationships are explored using a computational approach with microstructure quantification, dimensionality reduction, and finite element method (FEM) simulations. The FEM of representa-tive volume element (RVE) with a microstructure-based constitutive model model is proposed for predicting the overall stress-strain behavior of the investigated dual-phase steels. Then, Gaussian process regression (GPR) is used to make connections between the latent space point and the ferrite grain size as inputs and mechanical properties as outputs. The GPR with VAE successfully predicts the newly generated microstructures with target mechanical properties with high accuracy. This work demonstrates that a variety of microstructures can be can-didates for designing the optimal material with target properties in a continuous manner. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
The qualification of engineering materials requires extensive testing of the time-dependent performance of the material, namely the fatigue behavior. Model-based approaches for determining the fatigue behavior have pr...
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The qualification of engineering materials requires extensive testing of the time-dependent performance of the material, namely the fatigue behavior. Model-based approaches for determining the fatigue behavior have presented a tangible step towards complementing and reducing the overall number of physical tests necessary to qualify a material for use in application. Yet, prior to the adoption of the model-based approaches, the model needs to be thoroughly validated, presenting challenges, including substantiating the model's ability to capture the correct physics for crack initiation, which is difficult provided the multiple length-scales of the problem. In this paper, a methodology and demonstration for validating the location of microstructure-sensitive fatigue crack initiation as predicted by crystal plasticity finite element (CPFE) simulations, using high-energy X-ray diffraction and tomography experiments are presented. Realistic 3D microstructural models are created for the material of interest, IN718 (produced via additive manufacturing), with different twin instantiations, based on the experimental data for use in the CPFE simulations. The location of failure predicted using the extreme values of failure metrics (plastic strain accumulation and plastic strain energy density) resulted in an unambiguous one-to-one correlation with the experimentally observed location of crack-initiation for the models with statistical twin instantiations. (C) 2020 The Author(s). Published by Elsevier Ltd.
The magnetorheological elastomers (MREs) are novel multifunctional materials wherein their viscoelastic properties can be varied instantly under an application of applied magnetic field. Due to their field-dependent s...
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The magnetorheological elastomers (MREs) are novel multifunctional materials wherein their viscoelastic properties can be varied instantly under an application of applied magnetic field. Due to their field-dependent stiffness and damping properties, MREs are widely used in the development and design of MRE-based adaptive vibration isolators and absorbers and also biomedical engineering. Moreover, MREs due to their inherent magnetostriction effect have enormous potential for the development of soft actuators. The dynamic behavior of MREs is affected by various material parameters (e.g., matrix and particle types, particle concentration, additives) as well as mechanical and magnetic loading parameters (e.g., frequency, amplitude, temperature, magnetic flux density). Understanding and predicting the effect of materials and loading parameters on the response behavior of MREs are of paramount importance for the design of MRE-based adaptive structures and systems. This review paper mainly aims to provide a comprehensive study of material constitutive models to predict the nonlinear magnetomechanical behavior of MREs. Particular emphasis is paid to physics-based models including continuum- and microstructure-based models. Moreover, phenomenological models describing the dynamic magnetoviscoelastic behavior of MREs as well as the effect of temperature on the magnetomechanical behavior of such materials are properly addressed.
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