• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.8
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• pp.786-792
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• 2009

Topology optimization is to find the optimal material distribution of the specified design domain minimizing the objective function while satisfying the design constraints. Since the genetic algorithm (GA) has its advantage of locating global optimum with high probability, it has been applied to the topology optimization. To guarantee the structural connectivity, the concept of compliance pattern is proposed and to improve the convergence rate, small number of population size and variable probability in genetic operators are incorporated into GA. The rank sum weight method is applied to formulate the fitness function consisting of compliance, volume, connectivity and checkerboard pattern. To substantiate the proposed method design examples in the previous works are compared with respect to the number of function evaluation and objective function value. The comparative study shows that the compliance pattern based GA results in the reduction of computational cost to obtain the reasonable structural topology.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.3
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• pp.336-343
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• 2015

One of various main jobs in the design of a new tactical vehicle is to develop the lightest chassis parts satisfying the required durability target. In this study, the analytic methods to reduce the size and weight of a lower control arm and chassis frame of a Korean light tactical vehicle are presented. Topology optimization by ATOM (Abaqus Topology Optimization Module) is applied to find the optimal design of the suspension arm with volume and displacement constraints satisfied. In case of chassis frame, the light-weight optimization process associated with design sensitivity method is developed using Isight and ABAQUS. By these analytic methods we can provide design engineers with guides to where and how much the design changes should be made.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.12
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• pp.1811-1820
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• 2010

Most components in the real world show nonlinear response. The nonlinearity may arise because of contact between the parts, nonlinear material, or large deformation of the components. Structural optimization considering nonlinearities is fairly expensive because sensitivity information is difficult to calculate. To overcome this difficulty, the equivalent load method was proposed for nonlinear response optimization. This method was originally developed for size and shape optimization. In this study, the equivalent load method is modified to perform topology optimization considering all kinds of nonlinearities. Equivalent load is defined as the load for linear analysis that generates the same response field as that for nonlinear analysis. A simple example demonstrates that results of the topology optimization using equivalent load are very similar to the numerical results. Nonlinear response topology optimization is performed with a practical example and the results are compared with those of conventional linear response topology optimization.

• Structural Engineering and Mechanics
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• v.73 no.4
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• pp.463-479
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• 2020

This paper is aimed to address a simultaneous optimization of the size, shape, and topology of steel lattice towers through a combination of the radial basis function (RBF) neural networks and the artificial bee colony (ABC) metaheuristic algorithm to reduce the computational time because mere metaheuristic optimization algorithms require much time for calculations. To verify the results, use has been made of the CIGRE Tower and a 132 kV transmission towers as numerical examples both based on the design requirements of the ASCE10-97, and the size, shape, and topology have been optimized (in both cases) once by the RBF neural network and once by the MSTOWER analyzer. A comparison of the results shows that the neural network-based method has been able to yield acceptable results through much less computational time.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.137-145
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• 2002

Electric vehicle body has to be subjected to uniform load and requires auxiliary equipment such as air pipe and electric wire pipe. Especially, the cross beam supports the weight of passenger and electrical equipments. a lightweight vehicle body is salutary to save operating costs and fuel consumption. Therefore this study is to perform the size and the shape optimization of crossbeam for electric vehicle using the method of topology optimization to introduce the concept of homogenization based on optimality criteria method which is efficient for the problem having the number of design variables and a few boundary condition. this provides the method to determine the optimum position and shape of circular hole in the cross beam and then can achieve the optimal design to reduce weight.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.1015-1018
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• 2006

In this paper, new topology optimization technique is proposed. It mainly uses the strain energy distributions induced by the mode shapes associated with natural frequencies of the structure and so we can implicitly consider the dynamic characteristics of the structure in the topology optimization process. The strain energy to be minimized is employed as the objective function and the initial volume of structures is adopted as the constraint function. The resizing algorithm devised from the optimality criteria method is used to update the hole size of the cell existing in each finite element. The cantilever beam problem is adopted to test the proposed techniques. From numerical test, it is found to be that the optimum topology of the cantilever produced by the proposed technique has a hugh increase of natural frequency value and the technique is very effective to maximize the fundamental frequency of the structure.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.22-29
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• 2005

A material cloud method, which is a new topology optimization method, is presented. In MCM, an optimal structure can be found out by manipulating sizes and positions of material clouds, which are lumps of material with specified properties. A numerical analysis for a specific distribution of material clouds is carried out using fixed background finite element mesh. Optimal material distribution can be element-wisely extracted from material clouds' distribution. In MCM, an expansion-reduction procedure of design domain for finding out better optimal solution can be naturally realized. Also the convergence of material distribution is faster and well-defined material distribution with fewer intermediate densities can be obtained. In addition, the control of minimum-member sizes in the material distribution can be realized to some extent. In this paper, basic concept of MCM is introduced, and formulation and optimization results of MCM are compared with those of the traditional density distribution method(DDM).

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.11
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• pp.1118-1127
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• 2008

The position and size of holes in the partition of a double cavity are known to strongly affect the eigenfrequency of the longitudinal eigenmodes of the double cavity. To maximize or minimize the eigenfrequency of the hole-partitioned double cavity, two acoustical topology optimization problems are formulated and solved. While two sub-cavities are filled with air, a partition between them is assumed to consist of sub-partitions of variable acoustical properties. One design variable is assigned to each sub-partition, whose material properties are interpolated as those of an intermediate material between air and a rigid body. The penalty parameter of the used interpolation function is adjusted to obtain a distinct air and rigid body distribution at the converged stage in each acoustical topology optimization problem. A special attention is paid to the selection of initial values of design variables to obtain solutions as close to global optimum and symmetric as possible. To show numerical characteristics of these optimization problems, the formulated problems are first solved for the one-dimensional partition design domain and then for the two-dimensional partition design domain.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.1
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• pp.73-80
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• 2012

It is important to design the optimal shape in the initial process because the influences on the design and construction are large according to the shape and pattern of spatial structures. However, the existing optimal shape designs for spatial structure are performed by the designer's intuition and experiences. Therefore, this study proposes the integrated process using the topology optimization and cellular automata model. First, the initial optimal shapes are obtained by using the topology optimization, and then the spatial truss structural patterns are created through the application of cellular automata rules. Finally, the optimal shapes to satisfy the various design conditions are generated by the structural analysis and size optimization.

• Journal of Korean Society of Steel Construction
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• v.13 no.6
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• pp.673-681
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• 2001

The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithm. The algorithm can perform both shape and topology optimum designs of trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithm. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the design points selected form the genetic process. The evolutionary process evaluates the survivability of the design points. The evolutionary process evaluates the survivability of the design points selected form the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithm was verified by applying the algorithm to optimum design examples.