Direct detection of plasticity onset through total-strain profile evolution

TL;DR

This paper introduces a novel method to detect the onset of plasticity in materials by analyzing total strain fluctuations using statistical techniques like PCA and wavelet transforms, avoiding traditional elastic/plastic separation.

Contribution

It presents two new approaches for identifying yield points directly from total strain data in crystal plasticity models, applicable to polycrystalline materials.

Findings

Effective yield detection in synthetic polycrystal data.

Comparison of PCA and wavelet methods under different loading conditions.

Robustness of methods across strain-rate sensitivities.

Abstract

Plastic yielding in solids strongly depends on various conditions, such as temperature and loading rate and indeed, sample-dependent knowledge of yield points in structural materials promotes reliability in mechanical behavior. Commonly, yielding is measured through controlled mechanical testing at small or large scales, in ways that either distinguish elastic (stress) from total deformation measurements, or by identifying plastic slip contributions. In this paper we argue that instead of separate elastic/plastic measurements, yielding can be unraveled through statistical analysis of total strain fluctuations during the evolution sequence of profiles measured in-situ, through digital image correlation. We demonstrate two distinct ways of precisely quantifying yield locations in widely applicable crystal plasticity models, that apply in polycrystalline solids, either by using principal component analysis or discrete wavelet transforms. We test and compare these approaches in synthetic data of polycrystal simulations and a variety of yielding responses, through changes of the applied loading rates and the strain-rate sensitivity exponents.