Strain rate and temperature dependent fracture of aluminum alloy 7075: Experiments and neural network modeling

Pandya, Kedar S.; Roth, Christian C.; Mohr, Dirk

doi:10.1016/j.ijplas.2020.102788

Cited by 108 publications

(19 citation statements)

References 62 publications

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“…This value was chosen because the necking pattern has fully developed for all the velocities and microstructures investigated, yet the strains are not so large that the finite element grid has become significantly deformed. We are aware that assuming a constant value for the critical failure strain is a crude assumption, since the failure strain of metallic materials is generally dependent on the stress state, the strain rate and the temperature (Pandya et al, 2020;Habib et al, 2019;Khan and Liu, 2012). However, using this simple failure criterion facilitates the analysis and the interpretation of results.…”

Section: Analysis and Results: Fragmentationmentioning

confidence: 99%

Finite element analysis to determine the role of porosity in dynamic localization and fragmentation: Application to porous microstructures obtained from additively manufactured materials

Marvi-Mashhadi

Vaz-Romero

Sket

et al. 2021

International Journal of Plasticity

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In this paper, we have performed a microstructurally-informed finite element analysis on the effect of porosity on the formation of multiple necks and fragments in ductile thin rings subjected to dynamic expansion. For that purpose, we have characterized by X-ray tomography the porous microstructure of 4 different additively manufactured materials (aluminium alloy AlSi 10 Mg, stainless steel 316L, titanium alloy Ti 6 Al 4 V and Inconel 718L) with initial void volume fractions ranging from ≈ 0.0007% to ≈ 2%, and pore sizes varying between ≈ 6 µm and ≈ 110 µm. Three-dimensional analysis of the tomograms has revealed that the voids generally have nearly spherical shape and quite homogeneous spatial distribution in the bulk of the four materials tested. The pore size distributions quantified from the tomograms have been characterized using a Log-normal statistical function, which has been used in conjunction with a Force Biased Algorithm that replicates the experimentally observed random spatial distribution of the voids, to generate ring expansion finite element models in ABAQUS/Explicit (2016) which include actual porous microstructures representative of the materials tested. We have modeled the materials behavior using von Mises plasticity, and we have carried out finite element calculations for both elastic perfectly-plastic materials, and materials which show strain hardening, strain rate hardening and temperature softening effects. Moreover, we have assumed that fracture occurs when a critical value of effective plastic strain is reached. The finite element calculations have been performed for expansion velocities ranging from 50 m/s to 500 m/s. A key point of this investigation is that we have established individualized correlations between the main features of the porous microstructure (i.e. initial void volume fraction, average void size and maximum void size) and the number of necks and fragments formed in the calculations. In addition, we have brought out the effect of the porous microstrucure and inertia on the distributions of neck and fragment sizes. To the authors' knowledge, this is the first paper ever considering actual porous microstructures to investigate the role of material defects in multiple localization and dynamic fragmentation of ductile metallic materials.

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Section: Analysis and Results: Fragmentationmentioning

confidence: 99%

Finite element analysis to determine the role of porosity in dynamic localization and fragmentation: Application to porous microstructures obtained from additively manufactured materials

Marvi-Mashhadi

Vaz-Romero

Sket

et al. 2021

International Journal of Plasticity

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“…The uncoupled damage model is employed to simulate the ductile fracture of HSS. The fracture locus is a function of stress triaxiality and the Lode parameter or Lode angle parameters, such as the MMC model, 18 the Hosford–Coulomb model, 19,20 Lou–Huh model 21,22 and recent neural networks 23,24 . In this paper, the Lou–Huh model, 21,22 as expressed in Equation 13, is used to identify the fracture locus.…”

Section: Fracture Locus Identificationmentioning

confidence: 99%

“…Generally, the J2 plasticity model is to be used in combination with a separate fracture model. It is assumed that the fracture occurs at a point where a weighted measure of the accumulated equivalent plastic strain reaches a critical value, 14–16 such as the Johnson–Cook model, 17 the MMC model, 18 the Hosford–Coulomb model, 19,20 Lou–Huh model 21,22 and recent neural networks 23,24 . The critical equivalent plastic strain at the onset of fracture is the function of the stress triaxiality and the Lode (angle) parameter.…”

Section: Introductionmentioning

confidence: 99%

Ductile fracture locus identification using mesoscale critical equivalent plastic strain

Xin

Veljković

Correia

et al. 2021

Fatigue Fract Eng Mat Struct

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The ductile performance of high strength steel (HSS) from different steel grades, producers, manufacturing processes varies a lot. It is also difficult to conduct all kinds of reliable experiments to generate different stress status through different initial specimen geometries or by applying different load combinations in the civil engineering sector. One of the common issues for HSS structures is to identify the parameters of the ductile fracture model conveniently from the uniaxial stress–strain relationship obtained from common coupon specimens. An attempt is made in this paper to use the proposed mesoscale critical equivalent plastic strain (MCEPS) to calibrate the fracture locus of the uncoupled phenomenological model based only on the engineering stress–strain relationship. The proposed method is successfully validated by the Sandia fracture challenge in 2014.

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“…The phenomenological approach chosen for the development of this model leads to decoupling the effects of the strain rate, temperature and work hardening on the flow stress. However, experimental tests are still required to acquire material factors under different machining conditions [33,34].…”

Section: Introductionmentioning

confidence: 99%

Predictive Analytical Modeling of Thermo-Mechanical Effects in Orthogonal Machining

et al. 2021

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Factor relationships in a machining system do not work in pairs. Varying the cutting parameters, materials machined, or volumes produced will influence many machining characteristics. For this reason, we are attempting to better understand the effect of the Johnson-Cook (J-C) law of behavior on cutting temperature prediction. Thus, the objective of the present study is to investigate, experimentally and theoretically, the tool/material interactions and their effects on dust emission during orthogonal cutting. The proposed approach is built on three steps. First, we established an experimental design to analyze, experimentally, the cutting conditions effects on the cutting temperature under dry condition. The empirical model which is based on the response surface methodology was used to generate a large amount of data depending on the machining conditions. Through this step, we were able to analyze the sensitivity of the cutting temperature to different cutting parameters. It was found that cutting speed, tool tip radius, rake angle, and the interaction between the cutting speed and the rake angle explain more than 84.66% of the cutting temperature variation. The cutting temperature will be considered as a reference to validate the analytical model. Hence, a temperature prediction model is important as a second step. The modeling of orthogonal machining using the J-C plasticity model showed a good correlation between the predicted cutting temperature and that obtained by the proposed empirical model. The calculated deviations for the different cutting conditions tested are relatively acceptable (with a less than 10% error). Finally, the established analytical model was then applied to the machining processes in order to optimize the cutting parameters and, at the same time, minimize the generated dust. The evaluation of the dust generation revealed that the dust emission is closely related to the variation of the cutting temperature. We also noticed that the dust generation can indicate different phenomena of fine and ultrafine particles generation during the cutting process, related to the heat source or temperature during orthogonal machining. Finally, the effective strategy to limit dust emissions at the source is to avoid the critical temperature zone. For this purpose, the two-sided values can be seen as combinations to limit dust emissions at the source.

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Strain rate and temperature dependent fracture of aluminum alloy 7075: Experiments and neural network modeling

Cited by 108 publications

References 62 publications

Finite element analysis to determine the role of porosity in dynamic localization and fragmentation: Application to porous microstructures obtained from additively manufactured materials

Finite element analysis to determine the role of porosity in dynamic localization and fragmentation: Application to porous microstructures obtained from additively manufactured materials

Ductile fracture locus identification using mesoscale critical equivalent plastic strain

Predictive Analytical Modeling of Thermo-Mechanical Effects in Orthogonal Machining

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