• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.28 no.7
• /
• pp.885-895
• /
• 2004

Generally, automatic design is carried out with computer simulation and the simulation models are established by investigating the correlations between the simulation and real experiments. Therefore, the experiment results are utilized as complimentary data although they are considered to be precise. Orthogonal arrays have been adopted for discrete design. A method is proposed to directly exploit the experiment results in the design process with orthogonal arrays. Experiments are allocated to some rows of an orthogonal array and computer simulations are allocated to the others. A rule for the allocation is found to keep the orthogonality. Error analysis of the design results is performed. Mathematical examples are made to verify the validity of the proposed method. Error models are defined with the examples and the design solutions from the examples are discussed.

• Proceedings of the KSME Conference
• /
• 2003.11a
• /
• pp.1846-1851
• /
• 2003

Approximate optimization has become popular in engineering field such as MDO and Crash analysis which is time consuming. To accomplish efficient approximate optimization, accuracy of approximate model is very important. As surrogate model, Kriging have been widely used approximating highly nonlinear system . Because Kriging employs interpolation method, it is adequate for deterministic computer simulation. Because there are no random errors and measurement errors in deterministic computer simulation, instead of classical DOE ,space filling experiment design which fills uniformly design space should be applied. In this work, various space filling designs such as maximin distance design, maximum entropy design are reviewed. And new design improving maximum entropy design is suggested and compared.

• Journal of Korean Society for Quality Management
• /
• v.34 no.2
• /
• pp.122-128
• /
• 2006

One feature of a computer simulation experiment, different from a physical experiment, is that the output is often deterministic. Moreover the codes are computationally very expensive to run. This paper deals with the design and analysis of computer experiments(DACE) which is a relatively new statistical research area. We model the response of computer experiments as the realization of a stochastic process. This approach is basically the same as using a spatial linear model. Applications to the optimal mechanical designing and model calibration problems are illustrated. Algorithms for selecting the best spatial linear model are also proposed.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.10 no.5
• /
• pp.630-639
• /
• 1998

The present research aimed at development of a computer code for the optimal design of ventilation system based on one-dimensional analysis of the air flow. Model experiment and three-dimensional flow analysis have been implemented to determine loss coefficients that were needed for the optimization technique. A research on optimum shape of ventilation shaft has been also carried out through the three-dimensional analysis of the flow.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.23 no.11 s.170
• /
• pp.1871-1877
• /
• 1999

An optimal design method using a response surface method for dynamic characteristics of a washer is presented. The proposed method uses the design of experiment and a computer model is used for the experiment. The value of the cost function is estimated using a computer model for each case of the design variable variation. An orthogonal array is used to obtain best cases to be considered with minimum number of experimentation. Using these experimental values, optimal design is performed using a response surface method. The method used in this paper can be applied to any complicated mechanical systems that can be modelled and analyzed by a computer program. The method is applied to the design of dynamic characteristics of a washer.

• Journal of Korean Institute of Industrial Engineers
• /
• v.40 no.4
• /
• pp.366-374
• /
• 2014

This study assessed and analysed a result of a product design experiment through Product Data Analytics (PDA), to find reasons for failure of some projects in the experiment. PDA is a computer-based data analysis that uses Product Data Management (PDM) databases as its operational databases. The study examines 20 product design projects in the experiment, which are prepared to follow same product development process by using an identical PDM system. The design result in the PDM database is assessed and analysed by On-Line Analytical Processing (OLAP) and data mining tools in PDA. The assesment and analysis reveals the lateness in creation of 3D CAD models as the main reason of the failure.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.11a
• /
• pp.851-856
• /
• 1996

In this paper, we introduce the design method using CAE(Computer Aided Engineering) which is profitable in the compatability and standardization of the developed product, and the reduction of construction time and price to develop and design a machine equipment. Particularly, we select the standard model to design or develop from the large machinery to the super precision one, extract the peculiar characters of the model by the close analysis of the physical and technical part, the experiment for the characteristics of objective dimensions by analogical mathematical analysis for previous results, and can induce the design model demanded by user investigating optimal data in the design previous We present the analogical algorithms and process method of design factors and restriction factors in the systematization design with computer. Then we analyze step functions for each systematization equipment and induce the process of technical data with actuator model.

• Journal of the Korean Statistical Society
• /
• v.31 no.3
• /
• pp.277-299
• /
• 2002

Most statistical designs, such as orthogonal designs and optimal designs, are based on a specific statistical model. It is very often that the experimenter does not completely know the underlying model between the response and the factors. In computer experiments, the underlying model is known, but too complicated. In this case we can treat the model as a black box, or model to be unknown. Both cases need a space filling design. The uniform design is one of space filling designs and seeks experimental points to be uniformly scattered on the domain. The uniform design can be used for computer experiments and also for industrial experiments when the underlying model is unknown. In this paper we shall introduce the theory and method of the uniform design and related data analysis and modelling methods. Applications of the uniform design to industry and other areas are discussed.

• International Journal of Reliability and Applications
• /
• v.2 no.3
• /
• pp.209-240
• /
• 2001

Computer-based simulation and analysis is used extensively in engineering for a variety of tasks. Despite the steady and continuing growth of computing power and speed, the computational cost of complex high-fidelity engineering analyses and simulations limit their use in important areas like design optimization and reliability analysis. Statistical approximation techniques such as design of experiments and response surface methodology are becoming widely used in engineering to minimize the computational expense of running such computer analyses and circumvent many of these limitations. In this paper, we compare and contrast five experimental design types and four approximation model types in terms of their capability to generate accurate approximations for two engineering applications with typical engineering behaviors and a wide range of nonlinearity. The first example involves the analysis of a two-member frame that has three input variables and three responses of interest. The second example simulates the roll-over potential of a semi-tractor-trailer for different combinations of input variables and braking and steering levels. Detailed error analysis reveals that uniform designs provide good sampling for generating accurate approximations using different sample sizes while kriging models provide accurate approximations that are robust for use with a variety of experimental designs and sample sizes.

• Proceedings of the KIPE Conference
• /
• 1998.10a
• /
• pp.107-112
• /
• 1998

In this paper, we introduce the design method using CAE(Computer Aided Engineering) which is profitable in the compatibility and standardization of the developed product, and the reduction of construction time and price to develop and design a machine equipment. Particularly, we select the standard model to design or develop from the large machinery to the super precision one, extract the peculiar characters of the model by the close analysis on the physical and technical part, the experiment for the characteristics of objective dimensions by analogical mathematical analysis for previous results, and can induce the design model demanded by user investigating optimal data in the design previous. We present the analogical algorithms and process method of design factors and restriction factors in the systematization design with computer. Then we analyze step functions for each systematization equipment and induce the process of technical data with actuator model.