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Lighter weight and lower cost have been pursued in automotive industry. Traditionally, metal sheets of uniform thickness are used for stamping or forming vehicle structural parts. For a desired structure, a metal sheet with varying thickness is desirable. It not only saves material but also increases design flexibility. For example, some areas of a cross member require thicker thicknesses to support localized, larger loading, while for other areas, where there is no localized loading, thinner thicknesses can be used to save material. Tailor Rolled Blank (TRB) is an emerging manufacturing technology which allows engineers to change blank thickness continuously within a sheet metal, virtually eliminating the need for welding local reinforcements in the part. TRB also provides simpler structural design due to smooth, rolled transitions, which prevent stress concentrations in the finished part.

This paper focuses on the development of a framework of nonlinear finite element model validation for vehicle crash simulation. Integrated computational and test-based methods were discussed for validating computational models under physical, informational and model uncertaintes. Several methods were investigated to quantify transient time-domain data (functional data). The concept of correlation index was proposed to determine the degree to which a model is an accurate representation of the real world from the perspective of the intended uses of the model. The methodologies developed in this paper can also be used for CAE model updating, parameter tuning, and model calibration.

In vehicle safety engineering, it is important to determine the severity of occupant injury during a crash. Computer simulations are widely used to study how occupants move in a crash, what they collide during the crash and thus how they are injured. The vehicle motion is typically defined for the occupant simulation by specifying a crash pulse. Many computer models used to analyze occupant kinematics do not calculate both vehicle motion and occupant motion at the same time. This paper presents a framework of response surface methodology for the crash pulse prediction and vehicle structure design optimization. The process is composed of running simulation at DOE sampling data points, generating surrogate models (response surface models), performing sensitivity analysis and structure design optimization for time history data (e.g., crash pulse).