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Kyung-Suk Kim

Publications and source records attributed to Kyung-Suk Kim.

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On Hyperelastic Crease

We present analyses of crease-formation and stability criteria for incompressible hyperelastic solids. A generic singular perturbation over a laterally compressed half-space creates a far-field eigenmode of three energy-release angular sectors separated by two energy-elevating sectors of incremental deformation. The far-field eigenmode braces the energy-release field of the surface flaw against the transition to a self-similar crease field, and the braced-incremental-deformation (bid) field has a unique shape factor that determines the creasing stability. The shape factor, which is identified by two conservation integrals that represent a subsurface dislocation in the tangential manifold, is a monotonically increasing function of compressive strain. For Neo-Hookean material, when the shape factor is below unity, the bid field is configurationally stable. When the compressive strain is 0.356, the shape factor becomes unity, and the bid field undergoes a higher-order transition to a crease field. At the crease-limit point, we have two asymptotic solutions of the crease-tip folding field and the leading-order far field with two scaling parameters, the ratio of which is determined by matched asymptotes. Our analyses show that the surface is stable against singular perturbation up to the crease limit point and becomes unstable beyond the limit. However, the flat state is metastable against a regular perturbation between the crease limit point and wrinkle critical point, which is a first-order instability point. We introduced a novel finite element method for simulating the bid field with a finite domain size. For Gent model, the strain-stiffening alters the shape factor dependence on the compressive strain, raising crease resistance. The new findings in crease mechanisms will help study ruga mechanics of self-organization and design soft-material structures for high crease resistance.

physics.app-ph

Dynamic fracture of a bicontinuously nanostructured copolymer: A deep-learning analysis of big-data-generating experiment

Here, we report measurements of detailed dynamic cohesive properties (DCPs) beyond the dynamic fracture toughness of a bicontinuously nanostructured copolymer, polyurea, under an extremely loading rate, from deep-learning analyses of a dynamic big-data-generating experiment. We first describe a new Dynamic Line-Image Shearing Interferometer (DL-ISI), which uses a streak camera to record optical fringes of displacement-gradient vs time profile along a line on sample's rear surface. This system enables us to detect crack initiation and growth processes in plate-impact experiments. Then, we present a convolutional neural network (CNN) based deep-learning framework, trained by extensive finite-element simulations, that inversely determines the accurate DCPs from the DL-ISI fringe images. For the measurements, plate-impact experiments were performed on a set of samples with a mid-plane crack. A Conditional Generative Adversarial Networks (cGAN) was employed first to reconstruct missing DL-ISI fringes with recorded partial DL-ISI fringes. Then, the CNN and a correlation method were applied to the fully reconstructed fringes to get the dynamic fracture toughness, 12.1kJ/m^2, cohesive strength, 302 MPa, and maximum cohesive separation, 80.5 um, within 0.4%, 2.7%, and 2.2% differences, respectively. For the first time, the DCPs of polyurea have been successfully obtained by the DL-ISI with the pre-trained CNN and correlation analyses of cGAN-reconstructed data sets. The dynamic cohesive strength is found to be nearly three times higher than the dynamic-failure-initiation strength. The high dynamic fracture toughness is found to stem from both high dynamic cohesive strength and high ductility of the dynamic cohesive separation.

cond-mat.mtrl-sci