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

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...

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Bibliographic Details
Published in:Scientific reports 2020-05, Vol.10 (1), p.8262-8262, Article 8262
Main Authors: Yang, Zijiang, Papanikolaou, Stefanos, Reid, Andrew C. E., Liao, Wei-keng, Choudhary, Alok N., Campbell, Carelyn, Agrawal, Ankit
Format: Article
Language:English
Subjects:
639/301/1034/1037
639/301/930/12
Composite materials
Compression
Deep learning
Deformation
Humanities and Social Sciences
Learning algorithms
Machine learning
MATERIALS SCIENCE
Mechanical properties
multidisciplinary
Neural networks
Plasticity
Population characteristics
Population density
Principal components analysis
Science
Science & Technology - Other Topics
Science (multidisciplinary)
Simulation
Strain
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-65157-z