Micromechanical model of nanoparticle compaction and shock waves in metal powders

Mayer, Alexander E.

doi:10.1016/j.ijplas.2021.103102

Cited by 15 publications

(5 citation statements)

References 70 publications

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“…High-velocity compaction (HVC) is considered a manufacturing process with high-energy impacts [1,2], which usually utilizes a hydraulic hammer to impact the powder at a speed of 3-30 m s −1 [3], and the powder is subjected to an instantaneous pressure peak of 100 GPa. Therefore, HVC can obtain higher density and better mechanical properties than conventional compaction [4,5].…”

Section: Introductionmentioning

confidence: 99%

Characterization of energy dissipation of particle system in high velocity compaction

Yang,

Ma,

Xiong

et al. 2024

Phys. Scr.

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Energy transmission and dissipation during HVC lead to the uneven pressing force of the powder in the die cavity, which ultimately affects the densification and mechanical properties of powder metallurgy products. This study used the discrete element method (DEM) to monitor the energy transmission and dissipation of the powder system during HVC, investigate the relationship between the dissipation of kinetic energy and the impact energy during the loading stage, and consider the relationship between the strain energy release rate and the impact energy during the unloading stage. The results show that the boundary energy (impact energy) is mainly converted into strain, frictional, and damping energy, of which the kinetic energy is only an intermediate form and its attenuation equation is also obtained. The larger the porosity of the particle system is, the larger the dissipation factor β is, and the more serious the energy dissipation is. It can be found that the release rate of the strain energy gradually decreases with increasing impact energy, and the strain energy is significantly higher than the frictional energy in the green compacts.

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Section: Introductionmentioning

confidence: 99%

Characterization of energy dissipation of particle system in high velocity compaction

Yang,

Ma,

Xiong

et al. 2024

Phys. Scr.

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“…Previously, we had applied Bayesian calibration with the data of atomistic simulations for parameterization of mechanical models of several dynamic processes, including the dynamic compaction of nanoparticles [ 55 ] and nanoporous metals [ 56 ]. In all previous cases, the mechanical model worked quickly, and an emulator was not used.…”

Section: Introductionmentioning

confidence: 99%

Modified Taylor Impact Tests with Profiled Copper Cylinders: Experiment and Optimization of Dislocation Plasticity Model

et al. 2023

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Current progress in numerical simulations and machine learning allows one to apply complex loading conditions for the identification of parameters in plasticity models. This possibility expands the spectrum of examined deformed states and makes the identified model more consistent with engineering practice. A combined experimental-numerical approach to identify the model parameters and study the dynamic plasticity of metals is developed and applied to the case of cold-rolled OFHC copper. In the experimental part, profiled projectiles (reduced cylinders or cones in the head part) are proposed for the Taylor impact problem for the first time for material characterization. These projectiles allow us to reach large plastic deformations with true strains up to 1.3 at strain rates up to 105 s−1 at impact velocities below 130 m/s. The experimental results are used for the optimization of parameters of the dislocation plasticity model implemented in 3D with the numerical scheme of smoothed particle hydrodynamics (SPH). A Bayesian statistical method in combination with a trained artificial neural network as an SPH emulator is applied to optimize the parameters of the dislocation plasticity model. It is shown that classical Taylor cylinders are not enough for a univocal selection of the model parameters, while the profiled cylinders provide better optimization even if used separately. The combination of different shapes and an increase in the number of experiments increase the quality of optimization. The optimized numerical model is successfully validated by the experimental data about the shock wave profiles in flyer plate experiments from the literature. In total, a cheap, simple, but efficient route for optimizing a dynamic plasticity model is proposed. The dislocation plasticity model is extended to estimate grain refinement and volume fractions of weakened areas in comparison with experimental observations.

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“…MD simulations are directly applicable to very small spatial and temporal scales, which are on the verge of the modern experimental techniques with ultra-short laser-driven shocks [8][9][10][11] and far beyond the typical conditions of metal exploitation. Conversely, using of MD-generated data and machine learning methods for verification and parameterization of mechanical models [12][13][14] is a prospective route to extrapolate MD data to the scales of interest. In the present study we develop this approach regarding the spall fracture of copper.…”

Section: Introductionmentioning

confidence: 99%

Spall Fracture of Solid and Molten Copper: Molecular Dynamics, Mechanical Model and Strain Rate Dependence

Mayer

Pogorelko

Voronin

et al. 2022

Metals

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In this study, we formulate a mechanical model of spall fracture of copper, which describes both solid and molten states. The model is verified, and its parameters are found based on the data of molecular dynamics simulations of this process under ultrahigh strain rate of tension, leading to the formation of multiple pores within the considered volume element. A machine-learning-type Bayesian algorithm is used to identify the optimal parameters of the model. We also analyze the influence of the initial size distribution of pores or non-wettable inclusions in copper on the strain rate dependence of its spall strength and show that these initial heterogeneities explain the existing experimental data for moderate strain rates. This investigation promotes the development of atomistically-based machine learning approaches to description of the strength properties of metals and deepens the understanding of the spall fracture process.

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Micromechanical model of nanoparticle compaction and shock waves in metal powders

Cited by 15 publications

References 70 publications

Characterization of energy dissipation of particle system in high velocity compaction

Characterization of energy dissipation of particle system in high velocity compaction

Modified Taylor Impact Tests with Profiled Copper Cylinders: Experiment and Optimization of Dislocation Plasticity Model

Spall Fracture of Solid and Molten Copper: Molecular Dynamics, Mechanical Model and Strain Rate Dependence

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