The unique sensing features of the tilted Fibre Bragg Grating (TFBG) as a single three-parameter optical sensor are demonstrated in this work, to monitor the manufacturing process of composite materials produced using Vacuum Assisted Resin Transfer Moulding (VARTM) process. Each TFBG sensor can measure simultaneously and separately strain, temperature and refractive index (RI) of the material where the optical fibre is embedded. A TFBG embedded in a 2 mm glass-fibre/epoxy composite plate was used to measure the thermomechanical variations induced during the curing process. At the same time, the RI measurements, performed with the same TFBG sensor, can estimate the degree of cure of the resin. The TFBG sensor shows to be a valid and promising technology to improve the state of art of sensing and monitoring in composite material manufacturing.

The paper tackles the multi-objective optimisation of the cure stage of the Vacuum Assisted Resin Transfer Moulding (VARTM) process to manufacture the root insert of wind turbine blades. The aim of the study is to compare the Pareto front obtained from a pure heat transfer analysis, where temperature overshoot is an objective, with the one achieved when a coupled thermo-mechanical analysis is used, in which spring-in deflection is directly addressed. The optimisation methodology links the finite element solution of a heat transfer and a coupled thermo-mechanical problem of the cure process with a genetic algorithm suitable for multi-objective problems. The paper highlights that whilst minimising overshoot temperature is effective in reducing the spring-in, optimal design points might remain hidden. Furthermore, by showing the evolution of transverse residual stresses the paper suggests that designing cure cycles that introduce compressive stresses before vitrification is beneficial to counteract the subsequent tensile stresses generation.

The impetus for higher performance, robustness and efficiency in the aerospace, automotive and energy industries has been reflected in more stringent requirements which the composite manufacturing industry needs to comply with. The process design challenges associated with this are significant and can be only partially met by integration of simulation in the design loop. The implementation of numerical optimisation tools is therefore necessary. The development of methodologies linking predictive simulation tools with numerical optimisation techniques is pivotal to identify and therefore develop optimal design conditions that allow full exploitation of the efficiency opportunities in composite manufacturing. Numerical and experimental results concerning the optimisation techniques and methodologies implemented in literature to address the optimisation of thermoset composite manufacturing processes are presented and analysed in this study.

The paper deals with the influence of the convection coefficient and laminate thickness on multi-objective optimisation of the vacuum assisted resin transfer moulding cure stage for the manufacturing of wind turbine components. An epoxy resin system widely used in the wind turbine industry has been chemically characterised and the correspondent finite element implementation validated. The optimisation methodology developed links the finite element solution with a genetic algorithm and identifies a set of optimal cure cycles for a range of thicknesses (10–100 mm) able to minimise cure time (t _cure ) and the maximum degree of cure gradient developed through thickness (Δα _max ) during the cure stage as a measure of quality of the product. The results highlight that, by adding convection coefficient as design parameter of the process, significant benefits could be obtained when insulation is applied at the vacuum bag side for all thicknesses.

A stochastic multi-objective cure optimisation methodology is developed in this work and applied to the case of thick epoxy/carbon fibre laminates. The methodology takes into account the uncertainty in process parameters and boundary conditions and minimises the mean values and standard deviations of cure time and temperature overshoot. Kriging is utilised to construct a surrogate model of the cure substituting Finite Element (FE) simulation for computational efficiency reasons. The surrogate model is coupled with Monte Carlo and integrated into a stochastic multi-objective optimisation framework based on Genetic Algorithms. The results show a significant reduction of about 40% in temperature overshoot and cure time compared to standard cure profiles. This reduction is accompanied by a reduction in variability by about 20% for both objectives. This highlights the opportunity of replacing conventional cure schedules with optimised profiles achieving significant improvement in both process efficiency and robustness.