Automated search of a space of candidate designs is an attractive way
to improve the traditional engineering design process. To make this ap
proach work, however, an automated design system must include both kno
wledge of the modeling limitations of the method used to evaluate cand
idate designs and an effective way to use this knowledge to influence
the search process, We argue that a productive approach is to include
this knowledge by implementing a set of model constraint functions whi
ch measure how much each modeling assumption is violated. The search i
s then guided by using the values of these model constraint functions
as constraint inputs to a standard constrained nonlinear optimization
numerical method. A key result of our work is a successful demonstrati
on of the application of AI techniques to an important engineering pro
blem. In an empirical study of parametric conceptual aircraft design,
we observed a cost improvement of two orders of magnitude. The princip
al contribution of our work is a new design optimization methodology w
hich makes explicit the interaction between models of artifacts, and v
alidity models of artifact models. (C) 1998 Elsevier Science B.V. All
rights reserved.