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Multidisciplinary Design Optimization (MDO) has been widely used in the automotive industry to balance overall weight and stringent vehicle attributes, such as safety, NVH, durability, etc. To improve product quality and shorten product development cycle, a comprehensive MDO-based platform for vehicle attribute integration is developed in this paper. Some key issues in the platform development are addressed: Parameter model synchronization, Metamodel predictive capabilities, and Pre/post processing, etc. In addition, a strategy for body design is proposed to achieve weight targets while meeting other vehicle attributes. Lastly, the proposed methodology is demonstrated by a real world example for vehicle body design.

The body joint stiffness plays an important role in achieving vehicle attribute targets. One of the major drawbacks of joint stiffness evaluation is the lack of a rigorous criterion to assess whether the stiffness is proper for a body structure. This paper presents a general joint stiffness metric based on Hooke's law to better evaluate the stiffness of a body joint. A strategy for target setting of body joint stiffness was developed for vehicle body design. Finally, a vehicle body example was presented to demonstrate the proposed methodology.

Model validation is a process to assess the validity and predictive capabilities of a computer model by comparing simulation results with test data for its intended use of the model. One of the key difficulties for model validation is to evaluate the quality of a computer model at different test configurations in design space, and interpolate or extrapolate the evaluation results to untested new design configurations. In this paper, an integrated model interpolation and extrapolation framework based on Bayesian inference and Response Surface Models (RSM) is proposed to validate the designs both within and outside of the original design space. Bayesian inference is first applied to quantify the distributions' hyper-parameters of the bias between test and CAE data in the validation domain. Then, the hyper-parameters are extrapolated from the design configurations to untested new design. They are then followed by the prediction interval of responses at the new design points.