Transactions of the Korean Society of Mechanical Engineers A (대한기계학회논문집A)

The Korean Society of Mechanical Engineers (대한기계학회)
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Optimum Design Based on Sequential Design of Experiments and Artificial Neural Network for Enhancing Occupant Head Protection in B-Pillar Trim

센터 필라트림의 FMH 충격성능 향상을 위한 순차적 실험계획법과 인공신경망 기반의 최적설계

  • Lee, Jung Hwan (Global Research and Development Center, Mando Corporation) ;
  • Suh, Myung Won (School of Mechanical Engineering, Sungkyunkwan Univ.)
  • Received : 2013.07.09
  • Accepted : 2013.09.04
  • Published : 2013.11.01
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Abstract

The optimal rib pattern design of B-pillar trim considering occupant head protection can be determined by two methods. One is the conventional approximate optimization method that uses the statistical design of experiments (DOE) and response surface method (RSM). Generally, approximated optimum results are obtained through the iterative process by trial-and-error. The quality of results strongly depends on the factors and levels assigned by a designer. The other is a methodology derived from previous work by the authors, called the sequential design of experiments (SDOE), to reduce the trial-and-error procedure and to find an appropriate condition for using artificial neural network (ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently.

본 연구에서 탑승자 머리 보호를 위한 센터 필라 트림의 리브 패턴 최적설계는 두 가지 방법에 의해 수행된다. 첫째는 실험계획법과 반응표면법을 이용한 근사최적화 기법으로써, 상대적으로 큰 비중을 차지하는 해석비용 저감을 위하여 근사모델 구성에 필요한 최소한의 해석만을 수행하고 실제 최적화 과정에는 구성된 모델을 이용함으로써 근사적으로 최적 점을 찾아가는 방법이다. 하지만 이러한 방법은 시행착오적인 반복과정을 거쳐야 하는 단점이 있다. 따라서 저자들의 선행연구에서 제안한 순차적 실험계획법과 인공신경망을 이용하여 인자의 상한 또는 하한에 걸리지 않는 근사최적 해를 체계적인 반복과정을 통해 도출하고자 하며, 이를 수학적인 예제와 구조물 문제에 적용함으로써 실용성을 확인하고자 한다.

Keywords

References

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