• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2006년도 추계학술대회
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• pp.373-376
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• 2006

In general, there are three kinds of methods in analyzing dynamic robust design experiment: loss model approach, response function approach, and response model approach. In this talk, we review the three modeling approaches in terms of several criteria in comparison. This talk also generalizes the response model approach based on a generalized linear model. We develop a generalized two-step optimization procedure to substantially reduce the process variance by dampening the effect of both explicit and hidden noise variables. The proposed method provides more reliable results through iterative modeling of the residuals from the fitted response model. The method is compared with three existing approaches in practical examples.

• Journal of Computational Design and Engineering
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• 제2권4호
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• pp.248-252
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• 2015

Currently composite manufacturing process, such as linear friction welding plus NC machining, is the main method for the manufacturing and repairing of complex parts with integrated structure. Due to different datum position and inevitable distortion from different processes, it is important to ensure sufficient machining allowance for complex parts during the NC machining process. In this paper, a workpiece localization approach for machining allowance optimization of complex parts based on CMM inspection is developed. This technique concerns an alignment process to ensure sufficient stock allowance for the single parts as well as the whole integrated parts. The mathematical model of the constrained alignment is firstly established, and then the symmetric block solution strategy is proposed to solve the optimization model. Experiment result shows that the approach is appropriate and feasible to distribute the machining allowance for the single and whole parts for adaptive machining of complex parts.

• Steel and Composite Structures
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• 제32권4호
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• pp.455-466
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• 2019

Despite the rapid advancement in computing resources, many real-life design and optimization problems in structural engineering involve huge computation costs. To counter such challenges, approximate models are often used as surrogates for the highly accurate but time intensive finite element models. In this paper, surrogates for first-order shear deformation based finite element models are built using a polynomial regression approach. Using statistical techniques like Box-Cox transformation and ANOVA, the effectiveness of the surrogates is enhanced. The accuracy of the surrogate models is evaluated using statistical metrics like $R^2$, $R^2{_{adj}}$, $R^2{_{pred}}$ and $Q^2{_{F3}}$. By combining these surrogates with nature-inspired multi-criteria decision-making algorithms, namely multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), the optimal combination of various design variables to simultaneously maximize fundamental frequency and frequency separation is predicted. It is seen that the proposed approach is simple, effective and good at inexpensively producing a host of optimal solutions.

• Steel and Composite Structures
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• 제39권5호
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• pp.511-528
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• 2021

There are recently some advances in solving numerically topology optimization problems for large-scaled trusses based on ground structure approach. A disadvantage of this approach is that the final design usually includes many bars, which is difficult to be produced in practice. One of efficient tools is a so-called filter scheme for the ground structure to reduce this difficulty and determine several distinct bars. In detail, this technique is valuable for practical uses because unnecessary bars are filtered out from the ground structure to obtain a well-defined structure during the topology optimization process, while it still guarantees the global equilibrium condition. This process, however, leads to a singular system of equilibrium equations. In this case, the minimization of least squares with Tikhonov regularization is adopted. In this paper, a proposed algorithm in controlling optimal Tikhonov parameter is considered in combination with the filter scheme due to its crucial role in obtaining solution to remove numerical singularity and saving computational time by using sparse matrix, which means that the discrete optimal topology solutions depend on choosing the Tikhonov parameter efficiently. Several numerical examples are investigated to demonstrate the efficiency of the filter parameter control algorithm in terms of the large-scaled optimal topology designs.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.195-204
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• 2003

Network optimization is being increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. Networks provide a useful way to modeling real world problems and are extensively used in practice. Many real world applications impose on more complex issues, such as, complex structure, complex constraints, and multiple objects to be handled simultaneously and make the problem intractable to the traditional approaches. Recent advances in evolutionary computation have made it possible to solve such practical network optimization problems. The invited talk introduces a thorough treatment of evolutionary approaches, i.e., hybrid genetic algorithms approach to network optimization problems, such as, fixed charge transportation problem, minimum cost and maximum flow problem, minimum spanning tree problem, multiple project scheduling problems, scheduling problem in FMS.

• Journal of Mechanical Science and Technology
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• 제17권10호
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• pp.1496-1506
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• 2003

The SIMP (solid isotropic material with penalization) approach is perhaps the most popular density variable relaxation method in topology optimization. This method has been very successful in many applications, but the optimization solution convergence can be improved when new variables, not the direct density variables, are used as the design variables. In this work, we newly propose S-shape functions mapping the original density variables nonlinearly to new design variables. The main role of S-shape function is to push intermediate densities to either lower or upper bounds. In particular, this method works well with nonlinear mathematical programming methods. A method of feasible directions is chosen as a nonlinear mathematical programming method in order to show the effects of the S-shape scaling function on the solution convergence.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2005년도 추계 학술대회논문집
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• pp.1-10
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• 2005

A new approach, Multi-Objective Design Exploration (MODE), is presented to address Multidisciplinary Design Optimization (MDO) problems by CFD-CSD coupling. MODE reveals the structure of the design space from the trade-off information and visualizes it as a panorama for Decision Maker. The present form of MODE consists of Kriging Model, Adaptive Range Multi Objective Genetic Algorithms, Analysis of Variance and Self-Organizing Map. The main emphasis of this approach is visual data mining. An MDO system using high fidelity simulation codes, Navier-Stokes solver and NASTRAN, has been developed and applied to a regional-jet wing design. Because the optimization system becomes very computationally expensive, only brief exploration of the design space has been performed. However, data mining result demonstrates that design knowledge can produce a good design even from the brief design exploration.

• 대한기계학회논문집A
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• 제33권9호
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• pp.892-902
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• 2009

An efficient sequential optimization approach for metamodel was presented by Choi et al. This paper describes a new approach of the multilevel optimization method studied in Refs. [2] and [20,21]. The basic idea is concerned with multilevel iterative methods which combine a descent scheme with a hierarchy of auxiliary problems in lower dimensional subspaces. After fitting a metamodel based on an initial space filling design, this model is sequentially refined by the expected improvement criterion. The advantages of the method are that it does not require optimum sensitivities, nonlinear equality constraints are not needed, and the method is relatively easy to understand and use. As a check on effectiveness, the proposed method is applied to an engineering example.

• 한국자동차공학회논문집
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• 제2권2호
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• pp.33-41
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• 1994

The procedure to design the engine mount is briefly discussed and the optimum shape design process of engine mounting rubber using a parametric approach is suggested. An optimization code is developed to determine the shape to meet the stiffness requirements of engine mounts, coupled with the commercial nonlinear finite element program ABAQUS. A bush type engine mount used in a current passenger car is chosen for an application model. The shape from the result of the parameter optimization is determined as a final model with some modifications. The shape and stiffness of each optimization stage are shown and the stiffness of the optimized model along the principal direction is compared with the design specification of the current model. Finally, an overview of the current status and future works for the engine mount design are discussed.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2006년도 추계학술대회
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• pp.513-524
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• 2006

The ammunition allocation problem is a Multi-objective optimization(MOO) problem, maximizing fill-rate of multiple user troops and minimizing transportation time. Recent studies attempted to solve this problem by the prior preference articulation approach such as goal programming. They require that all the preference information of decision makers(DM) should be extracted prior to solving the problem. However, the prior preference information is difficult to implement properly in a rapidly changing state of war. Moreover they have some limitations such as heavy cognitive effort required to DM. This paper proposes a new ammunition allocation model based on more reasonable assumptions and uses an interactive MOO method to the ammunition allocation problem to overcome the limitations mentioned above. In particular, this article uses the GDF procedure, one of the well-known interactive optimization methods in the MOO liter-ature, in solving the ammunition allocation problem.