TOOLED THICK COMPOSITES by ARVEN H. SAUNDERS III ...

keep the cure on track. Examples of these rule-based systems are Qualitative Process
Automation for Autoclave Curing of Composites (QPA), Abrams et al (Abrams, 1983) and
SECURE, Cirisioli and Springer (Ciriscioli, 1990). QPA translates sensor information into a
qualitative cure state description (e.g., resin at minimum viscosity) by a "parser" which is
analyzed by an expert system. The expert system in turn makes and executes control decisions
to the autoclave to attain desired material properties. SECURE is similar to QPA in that it
interfaces expert system rules and sensor inputs while generating the required controller
outputs. These systems represent improvement over trial and error methods by imposing some
structure and discipline to what had previously been largely a “black art”. They also produced
more consistent yields and reduced cure cycle times. A limitation of such expert systems is that
they are very domain-specific in regards to the material system and process being used, as
opposed to being more general in nature.
Artificial neural networks (ANNs) have also been used by many researchers to capture
or “learn” a complex nonlinear relationship between input-output data. After the ANN has been
taught, it can be a very efficient representation of this mapping, and then could be employed
within a control system or optimization scheme to efficiently emulate the complex system it was
patterned after. Nielsen (Nielsen, 2002a) used a neural net to estimate the changing in-process
permeability of a fiber preform in a series combination with other model types to construct a
control system for resin transfer molding. ANNs are useful where abundant data exists for its
structured training, where input-output relationships are relatively static, and where there is no
interest in knowing or understanding the driving variables and the nature of their relationships to
outputs. However, ANNs have major shortcomings to deal with, such as learning and
overlearning, and how to incorporate new data. Another is the lack of transparency in the input-
output relationships that could provide useful insights into the nature of the problem domain.
Nielsen (Nielsen, 2001) applied physics-based process simulations to the training of an artificial
neural network (ANN). The ANN was then used for real-time process simulations, where a
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