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In recent years, the design of automotive components and assemblies have resulted in an over-reliance on advanced CAE tools especially the Finite Element Analysis. An emphasis on cost reduction and commonization of components in automotive industry has made it necessary to use the CAE tools in innovative ways. Use of FEA as a effective product development tool can be greatly enhanced if it provides a high degree of correlation with physical tests, thereby greatly limiting the investment in expensive prototypes and testing. This paper will discuss a robustness based methodology to realize effective correlation of finite element models with actual physical tests on automotive seat structure assembly, at a component, sub-system, and systems level. Based on a parameter design approach, the various factors that affect the degree of correlation between CAE models and physical tests will be described.

FMVSS 207/210 relates to seat system forward longitudinal strength and is one of the most important safety requirements for seats. Seat performance to satisfy FMVSS 207/210 can be simulated using LS-DYNA FEA code. When developing a seat design there is often a need to optimize the design to satisfy requirements/meet targets and to minimize weight. However LS-DYNA does not have optimization capabilities. This paper shows how the response surface based optimization can be used to meet FMVSS 207/210 requirements and reduce weight. A number of DOE runs are performed with different combinations of upper/lower/baseline gages. Data are collected for the maximum Von Misses stress and maximum effective plastic strain in each of the major seat parts along with the total weight of the seat. Based on the collected data the response surfaces are generated using Gaussian Stochastic Kriging method.