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

simulated annealing algorithm was used to derive the optimal real-time control decisions. A key
variable was estimation of in-situ preform permeability in real-time. Other researchers have
applied ANN models to experimental data, such as Ciurana et al (2009).
Traditional methods of optimization are gradient (derivative)-based. Regression models
(such as described by Kutner, 2005) are well established in linear and nonlinear forms, and well
suited for predicting the value of a single response variable in terms of independent variables or
factors. Response surface methodology (RSM) is probably best suited for a complex
multivariable situation, or where the exact nature of the functions is not known, or well
understood, as exemplified by Sheldon et al (Sheldon, 2001). Other researchers have applied
RSM to summarize input-output datasets generated by Monte Carlo simulations over ranges of
values for each input parameter to explore an n-dimensional space. Both regression and RSM
models can be developed based on sufficient experimental data.
Numerical based approaches to optimization, however, require knowledge of governing
equations, transfer functions, and so on. For a process that is complex, has many factors
suspected of being related to the response variable(s), and is not well understood, a
conventional approach for investigation is to use designed experiments. The most commonly
applied are a factorial or Taguchi experiment that is designed and carried out, featuring multiple
factors and levels, to explore the relationships to the critical outcomes, desired responses or
quality variables. Often analysis of variance (ANOVA) techniques are used to determine the
major or significant factors, most correlated to the responses. Experimental data may then be
fitted to regression or response surface models, with confirmation process runs used to validate
model predictions. Optimization can then be explored via the models. Examples include
Palanikumar (2008), Walia et al (2006) and Yang (2006).
State of the art cure process models typically utilize finite element models (FEMs) to
capture the full extent of its complexity. These models are widely used to provide high fidelity
physical models for process development and improvement studies, as by, for example, Chen
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