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곱분해 기법 기반의 통계 모멘트를 이용한 효율적인 강건 최적설계

Efficient Robust Design Optimization Using Statistical Moments Based on Multiplicative Decomposition Method

  • 조수길 (한양대학교 공과대학 미래자동차공학과) ;
  • 이민욱 (한양대학교 공과대학 미래자동차공학과) ;
  • 이태희 (한양대학교 공과대학 미래자동차공학과)
  • Cho, Su-Gil (Dept. of Automotive Engineering, College of Engineering, Hanyang Univ.) ;
  • Lee, Min-Uk (Dept. of Automotive Engineering, College of Engineering, Hanyang Univ.) ;
  • Lee, Tae-Hee (Dept. of Automotive Engineering, College of Engineering, Hanyang Univ.)
  • 투고 : 2012.09.23
  • 심사 : 2012.07.26
  • 발행 : 2012.10.01

초록

제품 생산 시 발생하는 제작 공차, 항복강도와 탄성계수 등 재료 물성치의 불확실성, 온도와 습도 같이 시스템에 작용하는 환경인자 등은 시스템의 성능에 영향을 미친다. 강건 최적설계는 이러한 인자들이 시스템에 미치는 영향을 최소화하면서 성능을 개선하는 설계기법으로 최근 많은 연구가 이루어지고 있다. 하지만 기존의 강건 최적설계 기법은 여러 인자들의 분포를 고려해야 하기 때문에 막대한 계산비용이 드는 문제가 있다. 본 논문에서는 이러한 문제점을 개선하기 위하여 곱분해 기법을 이용한 강건 최적설계를 제안한다. 제안된 기법을 이용하여 설계영역을 크리깅 메타모델로 근사하고 곱분해 기법을 적용하여 평균과 분산을 효율적이고 정확하게 계산하여 강건 최적설계를 수행한다. 제안된 방법을 수학예제와 공학예제에 적용하여 유용성을 검증한다.

The performance of a system can be affected by various variables such as manufacturing tolerances, uncertainties of material properties, and environmental factors acting on the system. Robust design optimization has attracted much attention in the design of products because it can find the best design solution that minimizes the variance of the response while considering the distribution of the variables. However, the computational cost and accuracy of optimization have thus far been a challenging problem. In this study, robust design optimization using the multiplicative decomposition method is proposed in order to solve these problems. Because the proposed method calculates the mean and variance of the system directly from the kriging metamodel using the multiplicative decomposition method, it can be used to search for a robust optimum design accurately and efficiently. Several mathematical and engineering examples are used to demonstrate the feasibility of the proposed method.

키워드

참고문헌

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피인용 문헌

  1. Failure Criterion of Straight Pipe with Outer Local Wall Thinning under Internal Pressure vol.18, pp.1, 2014, https://doi.org/10.9726/kspse.2014.18.1.076