Abstract:
This study focuses on the development of a multi-objective optimisation
methodology for the vacuum assisted resin transfer moulding composite
processing route. Simulations of the cure and filling stages of the process have
been implemented and the corresponding heat transfer and flow through porous
media problems solved by means of finite element analysis. The simulations
involved material sub-models to describe thermal properties, cure kinetics and
viscosity evolution. A Genetic algorithm which constitutes the foundation for the
development of the optimisation has been adapted, implemented and tested in
terms of its effectiveness using four benchmark problems. Two methodologies
suitable for multi-objective optimisation of the cure and filling stages have been
specified and successfully implemented. In the case of the curing stage the
optimisation aims at finding a cure profile minimising both process time and
temperature overshoot within the part. In the case of the filling stage the thermal
profile during filling, gate locations and initial resin temperature are optimised to
minimise filling time and final degree of cure at the end of the filling stage.
Investigations of the design landscape for both curing and filling stage have
indicated the complex nature of the problems under investigation justifying the
choice for using a Genetic algorithm. Application of the two methodologies
showed that they are highly efficient in identifying appropriate process designs
and significant improvements compared to standard conditions are feasible. In
the cure process an overshoot temperature reduction up to 75% in the case of
thick component can be achieved whilst for a thin part a 60% reduction in
process time can be accomplished. In the filling process a 42% filling time
reduction and 14% reduction of degree of cure at the end of the filling can be
achieved using the optimisation methodology. Stability analysis of the set of
solutions for the curing stage has shown that different degrees of robustness
are present among the individuals in the Pareto front. The optimisation
methodology has also been integrated with an existing cost model that allowed
consideration of process cost in the optimisation of the cure stage. The
optimisation resulted in process designs that involve 500 € reduction in process
cost. An inverse scheme has been developed based on the optimisation
methodology aiming at combining simulation and monitoring of the filling stage
for the identification of on-line permeability during an infusion. The methodology
was tested using artificial data and it was demonstrated that the methodology is
able to handle levels of noise from the measurements up to 5 s per sensor
without affecting the quality of the outcome.