Learning local, quenched disorder in plasticity and other crackling noise phenomena

Papanikolaou, Stefanos

doi:10.1038/s41524-018-0083-x

Cited by 16 publications

(17 citation statements)

References 62 publications

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“…The average curvature drastically decreases as the sample size decreases i.e., a longer continuous transition from elastic to perfect plastic, in contrast to the expected discontinuous yielding of annealed structures (dashed lines). The observed nonlinear behavior is evidently related to the yield strength size effect in small volumes: while the ensemble average of the yield strength increases as w → 0 (see Figure 4b) for either annealed or loaded microstructures, the yield strength distribution (see Figure 4b) becomes drastically wider with system-size for loaded dislocation configurations, in a qualitative agreement with nanopillar compression phenomenology [37]. By comparing Figure 4a,b, one may notice that the yield stress distribution disparity mirrors the system-size dependence of the anelastic (nonlinear) average behavior.…”

Section: The Mechanical Response Of Finite Small Volumes In Multi-slisupporting

confidence: 65%

From Statistical Correlations to Stochasticity and Size Effects in Sub-Micron Crystal Plasticity

Song

Papanikolaou

2019

Metals

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Metals in small volumes display a strong dependence on initial conditions, which translates into size effects and stochastic mechanical responses. In the context of crystal plasticity, this amounts to the role of pre-existing dislocation configurations that may emerge due to prior processing. Here, we study a minimal but realistic model of uniaxial compression of sub-micron finite volumes. We show how the statistical correlations of pre-existing dislocation configurations may influence the mechanical response in multi-slip crystal plasticity, in connection to the finite volume size and the initial dislocation density. In addition, spatial dislocation correlations display evidence that plasticity is strongly influenced by the formation of walls composed of bound dislocation dipoles.

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Section: The Mechanical Response Of Finite Small Volumes In Multi-slisupporting

confidence: 65%

From Statistical Correlations to Stochasticity and Size Effects in Sub-Micron Crystal Plasticity

Song

Papanikolaou

2019

Metals

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“…In this context, one might use an independent machine-learning method proposed in Ref. 21 and a stress tensor i which is connected to the reversible (elastic) strain tensor via the tensor of elastic constants, which we assume to represent an isotropic material. The internal microstructure of each element is characterized by a spectrum of stress dependent energy barriers of which we assume the lowest barrier, �E min,i ( i ) to control activation of irreversible deformation.…”

Section: Discussion and Outlookmentioning

confidence: 99%

“…In this context, one might use an independent machine-learning method proposed in Ref. 21 to infer local threshold distributions for matching simulations from non universal features of the experimental avalanche statistics.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Prediction of creep failure time using machine learning

Biswas

Castellanos

Zaiser

2020

Sci Rep

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A subcritical load on a disordered material can induce creep damage. The creep rate in this case exhibits three temporal regimes viz. an initial decelerating regime followed by a steady-state regime and a stage of accelerating creep that ultimately leads to catastrophic breakdown. Due to the statistical regularities in the creep rate, the time evolution of creep rate has often been used to predict residual lifetime until catastrophic breakdown. However, in disordered samples, these efforts met with limited success. Nevertheless, it is clear that as the failure is approached, the damage become increasingly spatially correlated, and the spatio-temporal patterns of acoustic emission, which serve as a proxy for damage accumulation activity, are likely to mirror such correlations. However, due to the high dimensionality of the data and the complex nature of the correlations it is not straightforward to identify the said correlations and thereby the precursory signals of failure. Here we use supervised machine learning to estimate the remaining time to failure of samples of disordered materials. The machine learning algorithm uses as input the temporal signal provided by a mesoscale elastoplastic model for the evolution of creep damage in disordered solids. Machine learning algorithms are well-suited for assessing the proximity to failure from the time series of the acoustic emissions of sheared samples. We show that materials are relatively more predictable for higher disorder while are relatively less predictable for larger system sizes. We find that machine learning predictions, in the vast majority of cases, perform substantially better than other prediction approaches proposed in the literature.

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“…In that work, dislocation classification was performed using Principal Component Analysis (PCA) 18 and continuous k-nearest neighbors clustering algorithms 19 . Analogous classifications of dislocation structures have since been extended in disordered dislocation environments 20 and three dimensional DDD samples 21 , as well as continuous plasticity models 22 using a variety of data science approaches. However, the problems in existing works are either in simpler models of dislocation dynamics 20 or with less challenging limits of the model (i.e.…”

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confidence: 99%

Learning to Predict Crystal Plasticity at the Nanoscale: Deep Residual Networks and Size Effects in Uniaxial Compression Discrete Dislocation Simulations

Yang

Papanikolaou

Reid

et al. 2020

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The density and configurational changes of crystal dislocations during plastic deformation influence the mechanical properties of materials. These influences have become clearest in nanoscale experiments, in terms of strength, hardness and work hardening size effects in small volumes. The mechanical characterization of a model crystal may be cast as an inverse problem of deducing the defect population characteristics (density, correlations) in small volumes from the mechanical behavior. In this work, we demonstrate how a deep residual network can be used to deduce the dislocation characteristics of a sample of interest using only its surface strain profiles at small deformations, and then statistically predict the mechanical response of size-affected samples at larger deformations. As a testbed of our approach, we utilize high-throughput discrete dislocation simulations for systems of widths that range from nano-to micro-meters. We show that the proposed deep learning model significantly outperforms a traditional machine learning model, as well as accurately produces statistical predictions of the size effects in samples of various widths. By visualizing the filters in convolutional layers and saliency maps, we find that the proposed model is able to learn the significant features of sample strain profiles. Prediction of mechanical behavior up to failure is commonly achieved using constitutive laws written as equations in terms of phenomenological parameters in the cases where physical laws are not clear or for the purpose of simplification. The parameters are obtained experimentally (at the manufacturing stage) for individual classes of materials, thus classified by composition, prior processing and load history, all of which affect the material's micro-structure and thus its behavior 1. Beyond processing routes, materials have been known to also be highly sensitive to micro-structure changes, especially when used at extreme conditions such as small volume, high temperature, high pressure, and high strain rates 2-11. Such extreme conditions are experienced in numerous applications at the technological and industrial frontiers. Thus, constitutive laws are difficult to apply when the micro-structural changes brought by operating conditions are unknown. In order to assess the yield and failure strength values, current practice requires non-destructive characterization methods at the nanoscale that can swiftly assess mechanical properties. The case study in this work represents possibly one of the most challenging, but benchmarked 12 , applications of micromechanics. More specifically, we investigate, using synthetic data from discrete dislocation plasticity simulations 12-15 how such non-destructive characterization can effectively work in the realistic scenario of assessing and predicting the strength of small finite volumes by using digital image correlation (DIC) 16 techniques. Changes in the dislocation structure of a material caused by prior processing of a crystal may not be evident from a visual inspection o...

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Learning local, quenched disorder in plasticity and other crackling noise phenomena

Cited by 16 publications

References 62 publications

From Statistical Correlations to Stochasticity and Size Effects in Sub-Micron Crystal Plasticity

From Statistical Correlations to Stochasticity and Size Effects in Sub-Micron Crystal Plasticity

Prediction of creep failure time using machine learning

Learning to Predict Crystal Plasticity at the Nanoscale: Deep Residual Networks and Size Effects in Uniaxial Compression Discrete Dislocation Simulations

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