In the vehicle development process, tradeoffs between weight and cost, as well as performance requirements involving crash safety, NVH, vehicle dynamics, durability, and other attributes, need to be established. Multidisciplinary design optimization (MDO) is a key enabler to solve these complex tradeoff studies. At Ford Motor Company, MDO has been integrated into the global product development process. To comply with all performance, weight, and cost targets and to deliver reliable and robust design proposals in time, a toolset for automated cross-attribute optimization based on MATLAB has been developed. The toolset has been deployed successfully throughout various vehicle programs.

This presentation elaborates on the automated data generation functionality, unique data-mining and meta-modeling features, multiobjective optimization algorithms, and data visualization techniques.

In addition, a MATLAB based graphical user interface is discussed.