Gun-Jin Yoon
Seoul National University · 工学
研究室紹介
Professor Gun-Jin Yoon's research lab specializes in advanced materials and structural mechanics, focusing on the development of intelligent material models, smart sensing materials, and multifunctional polymeric systems. The lab explores constitutive modeling of complex material behavior using machine learning techniques, such as neural networks and diffusion models, for microstructure reconstruction and mechanical response prediction. Key research directions include mechanoluminescent sensors, vitrimeric polymers with self-healing capabilities, and micromechanical modeling of nanocomposites with interfacial effects. The lab also advances structural health monitoring through innovative damage detection algorithms and sensor integration strategies.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Abstract Cyclic behavior of materials is complex and difficult to model. A combination of hardening rules in classical plasticity is one possibility for modeling this complex material behavior. Neural network (NN) constitutive models have been shown in the past to have the capability of modeling complex material behavior directly from the results of material tests. In this paper, we propose a novel approach for NN‐based modeling of the cyclic behavior of materials. The proposed NN material model
In this paper, the stress sensing performance of two well-known mechanoluminescence (ML) sensing materials, (1) SrAl2O4:Eu (SAOE) and (2) SrAl2O4:Eu, Dy (SAOED), has been experimentally studied. Under the same input loadings and strain rates, changes of the light intensity have been characterized in terms of sensitivity, repeatability and linearity. Effects of the strain rate on the light intensity changes have also been investigated for both ML sensing materials. SAOED appears to perform better
Vitrimers, a class of polymeric networks that change their topology above a threshold temperature, have been investigated in recent years. In order to further extend their properties, in this research, we demonstrate disulfide exchange assisted polydimethylsiloxane (PDMS)- and graphene oxide (GO)-involved epoxy vitrimers, which exhibit a reduction in glass transition temperature and storage modulus with increase in flexural strain and low-temperature self-healing. Stress relaxation and Arrhenius
This paper proposes a microstructure reconstruction framework with denoising diffusion models for the first time. The novelty and strength of the proposed model lie in its universality and generality for the microstructure characterization and reconstruction (MCR) that can be applied to various types of composite materials. The applicability of the diffusion-based models is validated with several types of microstructures (e.g., polycrystalline alloy, carbonate, ceramics, copolymer, fiber composi
In this paper, an extended Mori-Tanaka (MT) model was proposed to evaluate effective stiffnesses of wavy carbon nanotube (CNT) nanocomposites with interface damage. The proposed model combined MT theory with linear spring model and wavy CNT model. We validated the proposed model by comparing analytically and numerically derived dilute strain concentration tensors for the extended MT and 3D finite element models, respectively. Interfacial compliances attributed to nonbonded interactions were esti
A two-stage damage detection method is proposed and demonstrated for structural health monitoring. In the first stage, the subset selection method is applied for the identification of the multiple damage locations. In the second stage, the damage severities of the identified damaged elements are determined applying SSGA to solve the optimization problem. In this method, the sensitivities of residual force vectors with respect to damage parameters are employed for the subset selection process. Th