Relating plasticity to dislocation properties by data analysis: scaling vs. machine learning approaches

Materials Theory

Research

Michael Zaiser¹  Stefan Hiemer¹  Haidong Fan² 

[1] Department of Materials Science, WW8-Materials Simulation, Friedrich-Alexander Universität Erlangen-Nürnberg, Dr.-Mack-Str. 77, 90762, Fürth, Germany;Department of Mechanics, Sichuan University, Chengdu, People’s Republic of China;

关键词: Dislocation plasticity;    Scaling invariance;    Machine learning;   

DOI  :  10.1186/s41313-022-00050-y

 received in 2022-07-14, accepted in 2022-12-13,  发布年份 2022

来源: Springer

【 摘 要 】

Plasticity modelling has long relied on phenomenological models based on ad-hoc assumption of constitutive relations, which are then fitted to limited data. Other work is based on the consideration of physical mechanisms which seek to establish a physical foundation of the observed plastic deformation behavior through identification of isolated defect processes (’mechanisms’) which are observed either experimentally or in simulations and then serve to formulate so-called physically based models. Neither of these approaches is adequate to capture the complexity of plastic deformation which belongs into the realm of emergent collective phenomena, and to understand the complex interplay of multiple deformation pathways which is at the core of modern high performance structural materials. Data based approaches offer alternative pathways towards plasticity modelling whose strengths and limitations we explore here for a simple example, namely the interplay between rate and dislocation density dependent strengthening mechanisms in fcc metals.

【 授权许可】

CC BY   
© The Author(s) 2022

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