• Proceedings of the Korean Nuclear Society Conference
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• 1999.05a
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• pp.25-25
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• 1999

• The monthly packaging world
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• s.354
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• pp.54-60
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• 2022

• Structural Engineering and Mechanics
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• v.5 no.5
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• pp.609-624
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• 1997

The optimization of cutouts in composite plates was investigated by implementing a procedure known as Evolutionary Structural Optimization. Perforations were introduced into a finite element mesh of the plate from which one or more cutouts of a predetermined size were evolved. In the examples presented, plates were rejected from around each evolving cutout based on a predefined rejection criterion. The limiting ply within each plate element around the cutout was determined based on the Tsai-Hill failure criterion. Finite element plates with values below the product of the average Tsai-Hill number and a rejection criterion were subsequently removed. This process was iterated until a steady state was reached and the rejection criterion was then incremented by an evolutionary rate and the above steps repeated until the desired cutout area was achieved. Various plates with differing lay-up and loading parameters were investigated to demonstrate the generality and robustness of this optimization procedure.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.558-563
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• 2003

The tracked vehicle travel on off rod and on rod. So the tracked vehicle need a sufficient stiffness and a lightweight. In this study we performed FEA for the track vehicle and performed topology optimization based on the results of FEA. The displacements of road wheel are used as displacement constraint for topology optimization. We performed topology optimization with the control of the frame size which is the results of topology optimization and suggested the shaped of the tracked vehicle bottom plate of topology optimization

• Journal of Korean Association for Spatial Structures
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• v.21 no.1
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• pp.61-70
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• 2021

This paper describes a novel zero-stress member selecting method for sizing optimization of truss structures. When a sizing optimization method with static constraints is implemented, the member stresses are affected sensitively with changing the variables. However, because some truss members are unaffected by specific loading cases, zero-stress states are experienced by the elements. The zero-stress members could affect the computational cost and time of sizing optimization processes. Feature selection approaches can be then used to eliminate the zero-stress member from the whole variables prior to the process of optimization. Several numerical truss examples are tested using the proposed methods.

• Journal of Mechanical Science and Technology
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• v.20 no.4
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• pp.494-504
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• 2006

This research proposes a reliability-based topology optimization (RBTO) using the finite element method. RBTO is a topology optimization based on probabilistic (or reliability) constraints. Young's modulus, thickness, and loading are considered as the uncertain variables and RBTO is applied to static and eigenvalue problems. The RBTO problems are formulated and a sensitivity analysis is performed. In order to compute probability constraints, two methods-RIA and PMA-are used. Several examples show the effectiveness of the proposed method by comparing the classical safety factor method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.8 s.239
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• pp.1051-1060
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• 2005

Nonlinear Response Optimization using Equivalent Static Loads (NROESL) method/algorithm is proposed to perform optimization of non-linear response structures. The conventional method spends most of the total design time on nonlinear analysis. The NROESL algorithm makes the equivalent static load cases for each response and repeatedly performs linear response optimization and uses them as multiple loading conditions. The equivalent static loads are defined as the loads in the linear analysis, which generates the same response field as those in non-linear analysis. The algorithm is validated for the convergence and the optimality. The proposed algorithm is applied to a simple mathematical problem to verify the convergence and the optimality.

• Magazine of the Korean Society of Agricultural Engineers
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• v.29 no.4
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• pp.73-92
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• 1987

Formulation of the geometric optimization for truss structures based on the elasticity theory turn out to be the nonlinear programming problem which has to deal with the cross-sectional area of the member and the coordinates of its nodes simultaneously. A few techniques have been proposed and adopted for the analysis of this nonlinear programming problem for the time being. These techniques, however, bear some limitations on truss shapes, loading conditions and design criteria for the practical application to real structures. A generalized algorithm for the geometric optimization of the truss structures, which can eliminate the above mentioned limitations, is developed in this study. The algorithm proposed utilizes the two-levels technique. In the first level which consists of two phases, the cross-sectional area of the truss member is optimized by transforming the nonlinear problem into SUMT, and solving SUMT utilizing the modified Newton Raphson method. In the second level, which also consists of two phases the geometric shape is optimized utillzing the unindirectional search technique of the Powell method which make it possible to minimize only the objective functlon. The algorithm proposed in this study is numerically tested for several truss structures with various shapes, loading conditions and design criteria, and compared with the results of the other algorithms to examine its applicability and stability. The numerical comparisons show that the two- levels algorithm proposed in this study is safely applicable to any design criteria, and the convergency rate is relatively fast and stable compared with other iteration methods for the geometric optimization of truss structures. It was found for the result of the shape optimization in this study to be decreased greatly in the weight of truss structures in comparison with the shape optimization of the truss utilizing the algorithm proposed with the other area optimum method.

• Journal of Powder Materials
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• v.24 no.3
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• pp.223-228
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• 2017

Nanopowders provide better details for micro features and surface finish in powder injection molding processes. However, the small size of such powders induces processing challenges, such as low solid loading, high feedstock viscosity, difficulty in debinding, and distinctive sintering behavior. Therefore, the optimization of process conditions for nanopowder injection molding is essential, and it should be carefully performed. In this study, the powder injection molding process for Fe nanopowder has been optimized. The feedstock has been formulated using commercially available Fe nanopowder and a wax-based binder system. The optimal solid loading has been determined from the critical solid loading, measured by a torque rheometer. The homogeneously mixed feedstock is injected as a cylindrical green body, and solvent and thermal debinding conditions are determined by observing the weight change of the sample. The influence of the sintering temperature and holding time on the density has also been investigated. Thereafter, the Vickers hardness and grain size of the sintered samples have been measured to optimize the sintering conditions.

• Structural Engineering and Mechanics
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• v.50 no.2
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• pp.151-161
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• 2014

In this paper, fuel tank of the locomotive ER 24 has been studied. Firstly the behavior of fuel and air during the braking time has been investigated by using a two-phase model. Then, the distribution of pressure on the surface of baffles caused by sloshing has been extracted. Also, the fuel tank has been modeled and analyzed using Finite Element Method (FEM) considering loading conditions suggested by the DIN EN 12663 standard and real boundary conditions. In each loading condition, high stressed areas have been identified. By comparing the distribution of pressure caused by sloshing phenomena and suggested loading conditions, optimization of the tank has been taken into consideration. Moreover, internal baffles have been investigated and by modifying their geometric properties, search of the design space has been done to reach the optimal tank. Then, in order to reduce the mass and manufacturing cost of the fuel tank, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Artificial Neural Networks (ANNs) have been employed. It is shown that compared to the primary design, the optimized fuel tank not only provides the safety conditions, but also reduces mass and manufacturing cost by %39 and %73, respectively.