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Journal of Mechanical Science and Technology

, Volume 32, Issue 11, pp 5339–5344 | Cite as

Statistically weighted maximin distance design

  • Su-gil Cho
  • Junyong Jang
  • Sanghyun Park
  • Tae Hee Lee
  • Minuk Lee
Article
  • 3 Downloads

Abstract

In a computational experiment, a metamodel, which is an approximation model, is widely used to perform optimization efficiently. The accuracy of a metamodel significantly depends on the way of choosing sample points. This process is known as the design of experiment (DOE). An important property of DOE is space filling that is developed to obtain information evenly on the overall design domain. However, space filling may be ineffective in optimization because this property does not consider output information. The proposed novel sequential DOE places more sample points in the neighborhood of the interested region in terms of optimization. The proposed method employs the weighted distance concept that considers output information. The weighted distance is evaluated through proposed parameters that are obtained from the basic statistical distribution of output information, e.g., probability density or cumulative distribution function, while satisfying space filling.

Keywords

Design of experiment Maximin distance design Space filling design Design optimization Metamodel 

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Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Su-gil Cho
    • 1
  • Junyong Jang
    • 2
  • Sanghyun Park
    • 1
    • 3
  • Tae Hee Lee
    • 3
  • Minuk Lee
    • 4
  1. 1.Technology Center for Offshore Plant IndustriesKorea Research Institute of Ships & Ocean Engineering (KRISO)DaejeonKorea
  2. 2.The 4th R&D Institute – 3rd DirectorateAgency for Defense Development (ADD)DaejeonKorea
  3. 3.Department of Automotive EngineeringHanyang UniversitySeoulKorea
  4. 4.Solver Development TeamVirtual MotionSeoulKorea

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