• Journal of Computational Design and Engineering
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• v.3 no.1
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• pp.71-90
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• 2016

Steel nitriding is a thermo-chemical process leading to surface hardening and improvement in fatigue properties. The process is strongly influenced by many different variables such as steel composition, nitrogen potential, temperature, time, and quenching media. In the present study, the influence of such parameters affecting physic-chemical and mechanical properties of nitride steels was evaluated. The aim was to streamline the process by numerical-experimental analysis allowing defining the optimal conditions for the success of the process. Input parameters-output results correlations were calculated through the employment of a multi-objective optimization software, modeFRONTIER (Esteco). The mechanical and microstructural results belonging to the nitriding process, performed with different processing conditions for various steels, are presented. The data were employed to obtain the analytical equations describing nitriding behavior as a function of nitriding parameters and steel composition. The obtained model was validated, through control designs, and optimized by taking into account physical and processing conditions.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.948-953
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• 2012

The characteristics of an induction motor vary with the number of parameters and the performance relationship between the parameters also is implicit. In case of the induction motor design, we generally should estimate many objective physical quantities in the optimization procedure. In this article, the multi objective design optimization based on genetic algorithm is applied for the three phase induction motor. The efficiency, starting torque, and material cost are selected for the objectives. The validity of the design results is also clarified by comparison between calculated results and measured ones.

• Korean Management Science Review
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• v.32 no.4
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• pp.69-80
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• 2015

The semiconductor industry is one of those in which the most intricate processes are involved and there are many critical factors that are controlled with precision in those processes. Naturally production scheduling in the semiconductor industry is also very complex and studied by the industry and academia for many years; however, still there are many issues left unclear in the problem. This paper proposes an multi-objective optimization-based scheduling method for semiconductor fabrication(fab). Two main objectives are throughput maximization and meeting target production quantities. The first objective aims to reduce production cost, especially the fixed cost incurred by a large investment constructing a new fab facility. The other is meeting customer orders on time and also helps a fab maintain stable throughput through controlled WIP balancing in the long run. The paper shows a trade-off structure between the two objectives through experimental studies, which provides industrial practitioners with useful references.

• Structural Engineering and Mechanics
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• v.24 no.2
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• pp.155-180
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• 2006

Many applications in mechanical design involve elastic bodies coming into contact under the action of the applied load. The distribution of the contact pressure throughout the contact interface plays an important role in the performance of the contact system. In many applications, it is desirable to minimize the maximum contact pressure or to have an approximately uniform contact pressure distribution. Such requirements can be attained through a proper design of the initial surfaces of the contacting bodies. This problem involves a combination of two disciplines, contact mechanics and shape optimization. Therefore, the objective of the present paper is to develop an integrated procedure capable of evaluating the optimal shape of contacting bodies. The adaptive incremental convex programming method is adopted to solve the contact problem, while the augmented Lagrange multiplier method is used to control the shape optimization procedure. Further, to accommodate the manufacturing requirements, surface parameterization is considered. The proposed procedure is applied to a couple of problems, with different geometry and boundary conditions, to demonstrate the efficiency and versatility of the proposed procedure.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.825-835
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• 2017

Significant improvements to methodologies on structural damage detection (SDD) have emerged in recent years. However, many methods are related to inversion computation which is prone to be ill-posed or ill-conditioning, leading to low-computing efficiency or inaccurate results. To explore a more accurate solution with satisfactory efficiency, a PSO-INM algorithm, combining particle swarm optimization (PSO) algorithm and an improved Nelder-Mead method (INM), is proposed to solve multi-sample objective function defined based on Bayesian inference in this study. The PSO-based algorithm, as a heuristic algorithm, is reliable to explore solution to SDD problem converted into a constrained optimization problem in mathematics. And the multi-sample objective function provides a stable pattern under different level of noise. Advantages of multi-sample objective function and its superior over traditional objective function are studied. Numerical simulation results of a two-storey frame structure show that the proposed method is sensitive to multi-damage cases. For further confirming accuracy of the proposed method, the ASCE 4-storey benchmark frame structure subjected to single and multiple damage cases is employed. Different kinds of modal identification methods are utilized to extract structural modal data from noise-contaminating acceleration responses. The illustrated results show that the proposed method is efficient to exact locations and extents of induced damages in structures.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.5
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• pp.93-99
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• 2000

In many dynamic structural optimization problems, the goal is to reduce the total weight of the structure without causing the resonance. Up to now, gradient informations(i.e., design sensitivity) have been used to achieve the goal. For some class of dynamic problems, especially coalescent eigenvalue Problems with multiobjective optimization, the design sensitivity analysis is too much complicated mathematically and numerically. Therefore, this article proposes a new technique fur structural dynamic modification using a mode modification method with Genetic Algorithm(GA). In GA formulation, fitness is defined based on penalty function approach. Design variables are iteratively improved by using genetic algorithm. Two numerical examples are shown, (ⅰ) a cantilevered plate, and (ⅱ) H-shaped structure. The results demonstrate that the proposed method is highly efficient.

• Proceeding of KASS Symposium
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• 2005.05a
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• pp.246-253
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• 2005

The design of structural engineering optimization is to minimize the cost. This problem has many objective functions formulating section and shape as a function of the included discrete variables. simulated annealing, genetic algerian and TABU algorithm are searching methods for optimum values. The object of this reserch is comparing the result of TABU algorithm, and verifying the efficiency of TABU algorithm in structural optimization design field. For the purpose, this study used a solid truss of 25 elements having 10 nodes, and size optimization for each constraint and load condition of Geodesic one, and shape optimization of Cable Dome for verifying spatial structures by the application of TABU algorithm

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.1038-1043
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• 2003

There are many industrial applications including thin-body structures such as fins. For the numerical modeling of radiation of sound from thin bodies, the conventional boundary element method (BEM) using the Helmholtz integral equation fails to yield a reliable solution. Therefore, many researchers have tried to solve the thin-body acoustic problems. In the area of the design sensitivity analysis (DSA) and optimization methods, however, there has been just a few study reported. Especially fur the thin-body acoustics, however, no further study in the DSA and optimization fields has been reported. In this research, the normal derivative integral equation is adopted as an analysis formulation in the thin-body acoustics, and then used for the sizing DSA and optimization. Since the gradient-based method is used for the optimization, it is important to have accurate gradients (design sensitivities) of the objective function and constraints with respect to the design variables. The DSA formulations are derived through chain-ruled derivatives using the finite element method (FEM) and BEM by using the direct differentiation and continuum variation concepts. The proposed approaches are implemented and validated using a numerical example.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.571-575
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• 2008

An engine cradle is a quite important structural assembly for supporting the engine, suspension and steering parts of vehicle and absorbing the vibrations during the drive and the shock in the car crash. Recently, the engine cradle having structural stiffness enough to support the surrounding parts and absorbing the shock of collision has been widely used. The hydroforming technology may cause many advantages to automotive applications in terms of better structural integrity of parts, reduction of production cost, weight reduction, material saving, reduction in the number of joining processes and improvement of reliability. We focus on increasing the durability and the dynamic performance of engine cradle. For realizing this objective, several optimization design techniques such as shape, size, and topology optimization are performed. This optimization scheme based on the sensitivity can provide distinguished performance improvement in using hydroforming.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1436-1441
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• 2003

Recently, simulated annealing algorithms have widely been applied to many structural optimization problems. In this paper, simulated annealing, boltzmann annealing, fast annealing and adaptive simulated annealing are applied to optimization of truss structures for improvement quality of objective function and number of function evaluation. These algorithms are classified by cooling schedule. The authors have changed parameters of ASA's cooling schedule and the influence of cooling schedule parameters on structural optimization obtained is discussed. In addition, cooling schedule of BA and ASA mixed is applied to 10 bar-truss structure.