Computational analysis of occupant safety has become an efficient tool to reduce the development time for a new product. Multi-body computer models (e.g. Madymo models) that simulate vehicle interior, restraint system and occupants in various crash modes have been widely used. To ensure public safety, many important injury numbers, such as head injury criteria, chest G, chest deflection, femur loads, neck load, and neck moment, are monitored. In the past, deterministic optimization methods have been employed to meet various safety regulations. Further emphasis on product quality and the consistency of product performance, uncertainties in modeling, simulation, and manufacturing, need to be considered. There are many difficulties involved in the optimization under uncertainty for occupant restraint systems, such as (1) highly nonlinear and noisy nature of occupant injury numbers; (2) large number of constraints; and (3) computational intensity to obtain the statistic information of injury numbers by the traditional Monte Carlo method. This paper investigates an integrated robust design approach for occupant restraint system by taking advantages of design of experiments, variable screening, stochastic meta-modeling, and genetic algorithm. An occupant restraint system is used as an example to demonstrate the methodology, however, the proposed method is applicable for all occupant restraint system design problems.

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