Prediction of creep failure time using machine learning

Biswas, Soumyajyoti; Castellanos, David Fernández; Zaiser, Michael

doi:10.48550/arxiv.2005.03514

Cited by 1 publication

(1 citation statement)

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“…16 , ML has been further applied to predicting stress-strain curves of plastically deforming crystals, 17 and learning the interaction kernel of dislocations. 18 Recently ML has also been applied to the closely related problems of, e.g., predicting the local yielding dynamics of dry foams, 19 the creep failure time of disordered materials, 20 as well as the occurrence times of "laboratory earthquakes". 21 By its nature, the problem of predicting the plastic deformation process of crystalline samples depends on details such as whether the crystal only contains pre-existing glissile dislocations (this was the case in Ref.…”

Section: Introductionmentioning

confidence: 99%

Machine learning depinning of dislocation pileups

Sarvilahti,

Skaugen,

Laurson

2020

Preprint

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We study a one-dimensional model of a dislocation pileup driven by an external stress and interacting with random quenched disorder, focusing on predictability of the plastic deformation process. Upon quasistatically ramping up the externally applied stress from zero the system responds by exhibiting an irregular stress-strain curve consisting of a sequence of strain bursts, i.e., critical-like dislocation avalanches. The strain bursts are power-law distributed up to a cutoff scale which increases with the stress level up to a critical flow stress value. There, the system undergoes a depinning phase transition and the dislocations start moving indefinitely, i.e., the strain burst size diverges. Using sample-specific information about the pinning landscape as well as the initial dislocation configuration as input, we employ predictive models such as linear regression, simple neural networks and convolutional neural networks to study the predictability of the simulated stress-strain curves of individual samples. Our results show that the response of the system -including the flow stress value -can be predicted quite well, with the correlation coefficient between predicted and actual stress exhibiting a non-monotonic dependence on strain. We also discuss our attempts to predict the individual strain bursts.

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