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A physics-based model and simple scaling law to predict the pressure dependence of single crystal spall strength
یک مدل مبتنی بر فیزیک و قانون مقیاس پذیری ساده برای پیش بینی وابستگی فشار از قدرت اسپل تک بلور-2020 A homogenized framework for ductile damage accounting for the effect of void growth on the thermomechanical response of single crystals under dynamic loading (CPD-FE) is developed. The current framework extends our prior work (Nguyen et al., 2017) by in- corporating the yield function of Han et al. (2013) for porous single crystals to govern the degradation of the macroscopic critical resolved shear stress. Validation of the model against direct numerical simulations shows a significant improvement in accuracy under conditions of macroscopic shear loading. The model parameters are calibrated to Kolsky bar (split-Hopkinson pressure bar) and plate impact experiments, and utilized to predict spall strength of single crystal copper in 100 orientation. Simulation results exhibit favor- able agreement with single crystal plate impact tests over a range of strain rates and shock compression pressures. These simulation results are used to further interpret previous ex- perimental observations on the rate and pressure sensitivity of spallation. Lastly, a simple analytical model for spall strength depending on the temperature, strain rate and pres- sure is proposed, which shows agreement with molecular dynamics (MD) simulations and experimental results. This analytical model of spall strength concisely captures the phys- ical mechanisms governing the effects of pressure, strain rate, and temperature on spall strength. Keywords: Crystal plasticity | Damage | Failure | Fracture | Strain rate | Spall | Shock | Void |
مقاله انگلیسی |
2 |
High strain rate micro-compression for crystal plasticity constitutive law parameters identification
میزان فشار بالا فشرده سازی میکرو برای شناسایی پارامترهای قانون سازنده انعطاف پذیری کریستال-2020 Microcompression tests were performed on single crystal copper micropillars at 10−2 s−1 and 102 s−1 in
the [100], [110] and [111] orientations, to provide inputs for crystal plasticity strain rate sensitive parameters
inverse identification. The identification procedure used full pillar geometry finite element simulations.
An identifiability indicator based on the cost functions hessian matrix approximate close to the
minimum was used to assess the uniqueness and stability of the identified coefficients. Experimental
microcompression tests displayed a strain rate sensitive behaviour in the [100] crystal orientation. The
[110] and [111] orientations were less sensitive and were not used for identification. Stress-strain curve
sensitivity plots revealed that the higher the strain rate, the better the sensitivity. This was attributed to
high strain rates concentration in the shear bands as the strain rate increases. Identification on experimental
data well represented the single crystal strain rate sensitivity in the [100] orientations. A unique solution
was found using only a single orientation. Keywords: Micropillar compression | Crystal plasticity finite element | Inverse identification | Identifiability analysis |
مقاله انگلیسی |
3 |
A micromorphic crystal plasticity model with the gradient-enhanced incremental hardening law
مدل انعطاف پذیری بلور میکرومورفیک با قانون سخت شدن افزایشی شیب-2020 A model of crystal plasticity is developed in which the effects of plastic flow non-uniformity are
described through the full dislocation density tensor. The micromorphic approach is used in
which the dislocation density tensor is represented by the curl of an independent constitutive
variable called microdeformation. The microdeformation tensor is enforced by an energetic
penalty term to be close to the actual plastic distortion tensor. The curl of microdeformation
tensor enters the constitutive model in two independent but complementary ways. First, it is an
argument of the free energy density function, which describes the kinematic-type hardening in
cyclic non-uniform deformation. Second, its rate influences the rates of critical resolved shear
stresses, which corresponds to additional isotropic hardening caused by incompatibility of the
plastic flow rate. The latter effect, missing in the standard slip-system hardening rule, is described
in a simple manner that does not require any extra parameter in comparison to the non-gradient
theory. In the proposed model there are two independent internal length scales whose interplay is
examined by means of 1D and 2D numerical examples of plastic shearing of a single crystal. Keywords: Gradient theory | Crystal plasticity | Dissipation | Length scale | Cyclic deformation | Numerical regularization |
مقاله انگلیسی |
4 |
Deep learning based predictive modeling for structure-property linkages
مدل سازی پیش بینی مبتنی بر یادگیری عمیق برای پیوندهای ساختار و ویژگی-2019 Crystal plasticity finite element method (CPFEM) based simulations have been traditionally used for analyses of deformation in metals. However, CPFEM simulations are computationally expensive, especially for problems like fatigue where analyses are based on deformation cycles. Moreover, correlations of structure-property linkages based on homogenization and localization are not easily conceived. In this work deep learning based models have been proposed that are able to predict macroscopic properties based on features extracted from the microstructure with minimal human bias. The model is able to predict property against a given structure within dual phase, isotropic elastic-plastic regime. A systematic approach for finding optimal depth and width of neural network has been identified that reduces the overall development effort. It is observed that in the absence of a large training dataset, performance of a convolutional neural network (CNN) model degrades if too many layers and/or too many neurons are used. The CNN model is able to identify soft and hard regions of microstructures and is able to correlate structure-property relation in forward sense i.e. for homogenization. In this work, it has been demonstrated that human intervention is not needed for feature extraction and selection leading to minimization of researcher’s bias. The drawback of CNN model interpretability is overcome by using Respond-CAM feature visualization. Keywords: Machine learning | Crystal plasticity | Convolutional neural networks | Micromechanics | Deep learning | ICME |
مقاله انگلیسی |