Elasticity and mechanical metamaterials
Can aging under load train a material's elasticity?
Open access · cc by · source: Europe PMC
Simply holding a disordered foam network squeezed for long enough makes it auxetic, meaning it shrinks sideways when compressed, because its most stressed links weaken the most.
Study at a glance
- Design
- Other — Laser-cut 2D EVA-foam networks aged in a confining box at set training strains, Poisson's ratio measured afterwards, plus simulations of spring networks whose stiffnesses weaken in proportion to stored energy.
- N
- No sample count reported; four kinds of 2D foam systems (jammed discs, jammed-derived networks, holey sheets, random triangular networks) tested across training strains and times.
- Population
- 2D disordered networks cut from ethylene vinyl acetate foam, and simulated spring networks
- Outcome
- Poisson's ratio (linear and nonlinear) as a function of training strain and aging time
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Key findings
Small training strains did little, but for training strains of magnitude 0.15 or more the Poisson's ratio eventually turned negative, and networks with more void space reached lower values. Fresh copies of aged geometry had a lower ratio than the original but not as low as the aged sample, so both geometry and bond stiffness changes contributed. Simulations reproduced the drift to negative Poisson's ratio and predicted a nonlinear response that is auxetic only at larger strains. Aging under shear encoded direction: the ratio dropped from about 0.4 to about 0.2 along the aging axis but rose to 0.8-0.9 in the perpendicular direction.
Methodology
The authors laser-cut four types of disordered 2D networks from foam sheet and aged them by confining them in a smaller rigid box for various times and strains, or by aging them under shear. After removing each sample they compressed it along one axis and measured its sideways deformation to get the Poisson's ratio. They also copied the geometry of an aged network into fresh foam to separate geometric from stiffness changes, and simulated spring networks in which each spring weakens at a rate set by its stored elastic energy.
Limitations
The number of samples and measurement uncertainties are not reported, and results come from one foam material (with a brief mention of 3D-printed polyurethane). The simulations model only the weakening of bond stiffness and deliberately set aside changes in rest length and particle rearrangements, so they are an idealised limit. The foam also partly relaxes back after unloading, so how permanent the trained properties are over long times is not established.
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