Il Yong Kim

Prof. Dr. Il Yong Kim

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Research Focus & Metrics

Academic Career Length

23Years (2004-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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20 December 2005 in Structural and Multidisciplinary Optimization
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IKIl Yong Kim
Il Yong Kim
OWOlivier L de Weck
Olivier L de Weck

12 April 2018 in Structural and Multidisciplinary Optimization
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Journal Article
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DLDaozhong Li
Daozhong Li
IKIl Yong Kim
Il Yong Kim

29 August 2014 in Structural and Multidisciplinary Optimization
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CLChao Li
Chao Li
IKIl Yong Kim
Il Yong Kim
JJJack Jeswiet
Jack Jeswiet

24 November 2017 in Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
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Journal Article
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CLChao Li
Chao Li
IKIl Yong Kim
Il Yong Kim
The algorithms for multi-material topology optimization were developed to solve compliance-minimization problems and applied to engineering problems in automotive concepts and lightweight design. Two small-scale problems of a long cantilever and a control arm were studied initially to verify the effectiveness of the developed algorithms and in-house program. Optimal solutions achieved by the multi-material topology optimization method developed were compared to their counterparts obtained by standard single-material topology optimization. To efficiently solve real-world engineering problems, the algorithms were further advanced to incorporate extrusion constraints and to handle multiple load cases. The effectiveness and the efficiency of the proposed method were demonstrated by the study of two real-world engineering problems: (a) the conceptual design of a cross-member for a chassis frame; and (b) the conceptual design of an automotive engine cradle. The two optimization design problems both involved complex geometries, design and non-design domains, prescribed regions with specific material allocations, multiple load cases, and manufacturing extrusion constraints. It was explicitly demonstrated that, for the same weight, the optimum designs achieved by the multi-material topology optimization method were stiffer than those achieved by standard single-material topology optimization.

26 November 2008 in Journal of Biomechanical Engineering
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IJIn Gwun Jang
In Gwun Jang
IKIl Yong Kim
Il Yong Kim
BKByung Man Kwak
Byung Man Kwak
In bone-remodeling studies, it is believed that the morphology of bone is affected by its internal mechanical loads. From the 1970s, high computing power enabled quantitative studies in the simulation of bone remodeling or bone adaptation. Among them, Huiskes et al. (1987, “Adaptive Bone Remodeling Theory Applied to Prosthetic Design Analysis,” J. Biomech. Eng., 20, pp. 1135–1150) proposed a strain energy density based approach to bone remodeling and used the apparent density for the characterization of internal bone morphology. The fundamental idea was that bone density would increase when strain (or strain energy density) is higher than a certain value and bone resorption would occur when the strain (or strain energy density) quantities are lower than the threshold. Several advanced algorithms were developed based on these studies in an attempt to more accurately simulate physiological bone-remodeling processes. As another approach, topology optimization originally devised in structural optimization has been also used in the computational simulation of the bone-remodeling process. The topology optimization method systematically and iteratively distributes material in a design domain, determining an optimal structure that minimizes an objective function. In this paper, we compared two seemingly different approaches in different fields—the strain energy density based bone-remodeling algorithm (biomechanical approach) and the compliance based structural topology optimization method (mechanical approach)—in terms of mathematical formulations, numerical difficulties, and behavior of their numerical solutions. Two numerical case studies were conducted to demonstrate their similarity and difference, and then the solution convergences were discussed quantitatively.

22 October 2019 in Structural and Multidisciplinary Optimization
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GSGraeme Sabiston
Graeme Sabiston
IKIl Yong Kim
Il Yong Kim