• Transactions of the Korean Society of Automotive Engineers
• /
• v.12 no.2
• /
• pp.123-130
• /
• 2004

The automobile suspension system is composed of parts that affect performances of a vehicle such as ride quality, handling characteristics, straight performance and steering effort, etc. Moreover, by using the finite element analysis the cost for the initial design step can be decreased. In the design of a suspension system, usually system vibration and structural rigidity must be considered simultaneously to satisfy dynamic and static requirements simultaneously. In this paper, we consider the weight reduction and the increase of the first eigen-frequency of a suspension part, the upper control arm, especially using topology optimization and size optimization. Firstly, we obtain the initial design to maximize the first eigen-frequency using topology optimization. Then, we apply the multi-objective parameter optimization method to satisfy both the weight reduction and the increase of the first eigen-frequency. The design variables are varying during the optimization process for the multi-objective. Therefore, we can obtain the deterministic values of the design variables not only to satisfy the terms of variation limits but also to optimize the two design objectives at the same time. Finally, we have executed reliability based optimal design on the upper control arm using the Monte-Carlo method with importance sampling method for the optimal design result with 98％ reliability.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.28 no.9
• /
• pp.1399-1407
• /
• 2004

Structural optimization has been carried out in continuous design space or in discrete design space. Generally, available designs are discrete in design practice. However, the methods for discrete variables are extremely expensive in computational cost. An iterative optimization algorithm is proposed for design in a discrete space, which is called a sequential algorithm using orthogonal arrays (SOA). We demonstrate verifying the fact that a local optimum solution can be obtained from the process with this algorithm. The local optimum solution is defined in a discrete design space. Then the search space, which is a set of candidate values of each design variables formed by the neighborhood of a current design point, is defined. It is verified that a local optimum solution can be found by sequentially moving the search space. The SOA algorithm has been applied to problems such as truss type structures. Then it is confirmed that a local solution can be obtained by using the SOA algorithm

• Journal of the Korean Society for Precision Engineering
• /
• v.28 no.11
• /
• pp.1297-1308
• /
• 2011

The light-weight design of UTM (Urban Transit Maglev)-02 car-body frames are performed, based on initial configuration. The thicknesses of fourteen sub-structures are defined as design variables and the loading condition is considered according to weight of sub-structures, electronic and pneumatic modules and passengers. For efficient and robust process of design optimization, objective function and constraints are approximated by response surface approximation. Structural analysis is performed at some sampling points to construct the approximated objective function and constraints composed of design variables. Design space is changed to find many optimal candidates and best optimal design can be found eventually. The Matlab Optimization Toolbox is used to find optimal value and sensitivity analysis about each design variable is also performed.

• IE interfaces
• /
• v.10 no.1
• /
• pp.169-187
• /
• 1997

Recently, in organizations many decisions are being made by groups. And the organization is changing a lot so are groups. To help decision making of changing groups, we need more flexible and more adaptive GDSS. Therefore one of the critical success factors of GDSS is flexibility and incremental improvement. Prior research on specifying design requirements of GDSS suggests generic design requirements. But they are too general to be incorporated directly into system design, because of the disparity between real group and ideal group that the researchers studied. Many design strategies that start from the generic design requirements thus have contingency variables that changes as the characteristics of group change. From the viewpoint of developers, these variables implicate the desirability of flexibility. To achieve flexibility we need new methodology of design and implementation. Nowadays, object-oriented analysis and design methodologies have been progressed to the point that many systems are being developed through these methodologies. In this paper, a design is proposed using Object-Oriented Modeling Techniques(OMT). Exploiting object-oriented paradigm results in a highly flexible and easily upgradable design.

• International Journal of Aeronautical and Space Sciences
• /
• v.18 no.4
• /
• pp.651-661
• /
• 2017

A truss-braced wing (TBW) aircraft has recently received increasing attention due to higher aerodynamic efficiency compared to conventional cantilever wing aircraft. For conceptual TBW aircraft design, we developed a propulsion-and-airframe integrated design environment by replacing a semi-empirical turbofan engine model with a thermodynamic cycle-based one built upon the numerical propulsion system simulation (NPSS). The constructed NPSS model benefitted TBW aircraft design study, as it could handle engine installation effects influencing engine fuel efficiency. The NPSS model also contributed to broadening TBW aircraft design space, for it provided turbofan engine design variables involving a technology factor reflecting progress in propulsion technology. To effectively consolidate the NPSS propulsion model with the TBW airframe model, we devised a rapid, approximate substitute of the NPSS model by reduced-order modeling (ROM) to resolve difficulties in model integration. In addition, we formed an artificial neural network (ANN) that associates engine component attributes evaluated by object-oriented weight analysis of turbine engine (WATE++) with engine design variables to determine engine weight and size, both of which bring together the propulsion and airframe system models. Through propulsion-andairframe design space exploration, we optimized TBW aircraft design for fuel saving and revealed that a simple engine model neglecting engine installation effects may overestimate TBW aircraft performance.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.24 no.8 s.179
• /
• pp.1940-1948
• /
• 2000

A design method for maximizing fatigue life of the deep groove ball bearing without enlarging mounting space is proposed by using a genetic algorithm. The use of gradient-based optimization methods for the design of the bearing is restricted because this design problem is characterized by the presence of discrete design variables such as the number of balls and standard ball diameter. Therefore, the design problem of rolling element bearings is a constrained discrete optimization problem. A genetic algorithm using real coding is used to efficiently find the optimum discrete design values. To effectively deal with the design constraints, a ranking method is suggested for constructing a fitness function in the genetic algorithm. Constrains for manufacturing are applied in optimization scheme. Results obtained for several 63 series deep groove ball bearings demonstrated the effectiveness of the proposed design methodology by showing that the average basic dynamic capacities of optimally designed bearings increased about 9-34% compared with the standard ones.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.13 no.2
• /
• pp.55-63
• /
• 2014

Fractures and wrinkles are two major defects frequently found in the sheet metal forming process. The process has several noise factors that cannot be ignored when determining the optimal process conditions. Therefore, without any countermeasures against noise, attempts to reduce defects through optimal design methods have often led to failure. In this study, a new and robust design methodology that can reduce the possibility of formation of fractures and wrinkles is presented using decision-making theory. A two-attribute value function is presented to form the design metric for the sheet metal forming process. A modified complex method is adopted to isolate the optimal robust design variables. One of the major limitations of the traditional robust design methodology, which is based on an orthogonal array experiment, is that the values of the optimal design variables have to coincide with one of the experimental levels. As this restriction is eliminated in the complex method, a better solution can be expected. The procedure of the proposed method is illustrated through a robust design of the sheet metal forming process of a side member of an automobile body.

• Korean Journal of Computational Design and Engineering
• /
• v.5 no.2
• /
• pp.127-135
• /
• 2000

Emergent computing paradigms such as genetic algorithms have found increased use in problems in engineering design. These computational tools have been shown to be applicable in the solution of generically difficult design optimization problems characterized by nonconvexities in the design space and the presence of discrete and integer design variables. Another aspect of these computational paradigms that have been lumped under the bread subject category of soft computing, is the domain of artificial intelligence, knowledge-based expert system, and machine learning. The paper explores a machine learning paradigm referred to as teaming classifier systems to construct the high-quality global function approximations between the design variables and a response function for subsequent use in design optimization. A classifier system is a machine teaming system which learns syntactically simple string rules, called classifiers for guiding the system's performance in an arbitrary environment. The capability of a learning classifier system facilitates the adaptive selection of the optimal number of training data according to the noise and multimodality in the design space of interest. The present study used the polynomial based response surface as global function approximation tools and showed its effectiveness in the improvement on the approximation performance.

• Journal of the Korean Society for Precision Engineering
• /
• v.27 no.12
• /
• pp.92-98
• /
• 2010

The design of press bonding tool in LCD module equipment is a very complex and difficult task because many design able variables are involved while their effects are not known. It takes longtime experiments and much expenses to verify the effects of these design variables. However the optimization of bonding tool using OLB(outer lead bonding) and PCB Bonding is a very important problem in LCD manufacturing process, so much design efforts have been made for improving the bonding tool performance. In this paper, a reasonable and fast process which gives optimized solution under the design requirements has been presented. Both analytical and statistical methods are employed in this process. A reliable analytic model using experiment-oriented FE analysis can be obtained, in which the regression equations that predict the tool efficiency from various DOE method are found. Improvement of tool efficiency could be estimated by the regression equations using meaningful factors converged by RSM(Response Surface Method). With this process a reasonable optimized solution that meets a variety of design requirements can be easily obtained.

• Proceedings of the KSME Conference
• /
• 2004.04a
• /
• pp.1005-1010
• /
• 2004

The structural optimization has been carried out in the continuous design space or in the discrete design space. Generally, available designs are discrete in design practice. But methods for discrete variables are extremely expensive in computational cost. In order to overcome this weakness, an iterative optimization algorithm was proposed for design in the discrete space, which is called as a sequential algorithm using orthogonal arrays (SOA). We focus to verify the fact that the local solution can be obtained throughout the optimization with this algorithm. The local solution is defined in discrete design space. Then the search space, which is the set of candidate values of each design variables formed by the neighborhood of current design point, is defined. It is verified that a local solution can be founded by moving sequentially the search space. The SOA algorithm has been applied to problems such as truss type structures. Then it is confirmed that a local solution can be obtained using the SOA algorithm