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


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.


Sequential Design of Experiments;Artificial Neural Network;Free Motion Headform(FMH);FMVSS


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