首页|Micropillar compression using discrete dislocation dynamics and machine learning
Micropillar compression using discrete dislocation dynamics and machine learning
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Discrete dislocation dynamics(DDD)simulations reveal the evolution of dislocation structures and the interaction of dislocations.This study investigated the compression behavior of single-crystal copper micropillars using few-shot machine learning with data provided by DDD simulations.Two types of features are considered:external features comprising specimen size and loading orientation and internal features involving dislocation source length,Schmid factor,the orientation of the most easily activated dislocations and their distance from the free boundary.The yielding stress and stress-strain curves of single-crystal copper micropillar are predicted well by incorporating both external and internal features of the sample as separate or combined inputs.It is found that the machine learning accuracy predictions for single-crystal micropillar compression can be improved by incorporating easily activated dislocation features with external features.However,the effect of easily activated dislocation on yielding is less important compared to the effects of specimen size and Schmid factor which includes information of orientation but becomes more evident in small-sized micropillars.Overall,incorporating internal features,especially the information of most easily activated dislocations,improves predictive capabilities across diverse sample sizes and orientations.
Discrete dislocation dynamics simulationsMachine learningSize effectsOrientation effectsMicrostructural features
Jin Tao、Dean Wei、Junshi Yu、Qianhua Kan、Guozheng Kang、Xu Zhang
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School of Mechanics and Aerospace Engineering,Southwest Jiaotong University,Chengdu 610031,China
State Key Laboratory of Nonlinear Mechanics,Institute of Mechanics,Chinese Academy of Sciences,Beijing 100190,China
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