Direct detection of plasticity onset through total-strain profile evolution
This work addresses the need for reliable yield point detection in structural materials, offering a novel approach that could enhance mechanical behavior prediction, though it appears incremental as it builds on existing digital image correlation techniques.
The paper tackles the problem of detecting the onset of plastic yielding in solids by proposing a method that analyzes total strain fluctuations from in-situ profile measurements, demonstrating two approaches (principal component analysis and discrete wavelet transforms) to precisely quantify yield locations in polycrystalline materials.
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.