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

TL;DR

This paper explores data-driven methods, including scaling and machine learning, to relate plasticity to dislocation properties, aiming to better capture the complex collective phenomena in deformation of high-performance materials.

Contribution

It compares scaling and machine learning approaches for modeling plasticity based on dislocation data, highlighting their capabilities and limitations.

Findings

Machine learning captures complex relationships better than simple scaling.

Data-driven models reveal insights into dislocation mechanisms in fcc metals.

Both approaches improve understanding of deformation pathways.

Abstract

Plasticity modelling has long been based on phenomenological models based on ad-hoc assuption 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.