  In this research, a new data mining-based design approach has been developed
for designing complex mechanical systems such as a crashworthy passenger car
with uncertainty modeling. The method allows exploring the big crash simulation
dataset to design the vehicle at multi-levels in a top-down manner (main energy
absorbing system, components, and geometric features) and derive design rules
based on the whole vehicle body safety requirements to make decisions towards
the component and sub-component level design. Full vehicle and component
simulation datasets are mined to build decision trees, where the
interrelationship among parameters can be revealed and the design rules are
derived to produce designs with good performance. This method has been extended
by accounting for the uncertainty in the design variables. A new decision tree
algorithm for uncertain data (DTUD) is developed to produce the desired designs
and evaluate the design performance variations due to the uncertainty in design
variables. The framework of this method is implemented by combining the design
of experiments (DOE) and crash finite element analysis (FEA) and then
demonstrated by designing a passenger car subject to front impact. The results
show that the new methodology could achieve the design objectives efficiently
and effectively. By applying the new method, the reliability of the final
designs is also increased greatly. This approach has the potential to be
applied as a general design methodology for a wide range of complex structures
and mechanical systems.
