Process Optimization of Composite Flex Beams using Neural Networks

Large scale, intricate shape parts with widely varying
cross-sections find difficulty in manufacturing using conventional
manufacturing processes. Cavity molding, a process similar to
compression molding is unique and often used in rotorcraft industries to
manufacture thick flex beam composite parts with high viscosity resins.
The curing of thermosetting resins used in these applications is
usually accompanied by an exothermic reaction and excessive heat buildup
during the polymerization reaction can cause internal stresses to
build-up and result in structural defects in thick composite structures.
Process optimization of flex beam composite parts manufactured using
the cavity molding process will result higher quality parts. In this
work, a three-dimensional coupled cure and flow model is developed to
investigate the cavity molding process. Comsol Multiphysics, a finite
element tool with multiphysics capabilities is used to study the coupled
cure and flow phenomenon. Finite element simulations are performed to
predict resin flow, resultant temperature distributions, and degrees of
cure at various cross-sections. Neural networks based models are
developed and integrated with finite element modeling to optimize the
cavity molding process, and the optimal process parameters are
suggested.

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