Abstract
Thermosetting polymers play a central role in composite manufacturing, where their cure kinetics governs the formation of crosslinked networks through thermally and chemically activated reactions. The degree of cure, strongly dependent on polymer chemistry, initiators, catalysts, and processing conditions such as temperature, time, heating rate, and humidity, directly influences the mechanical performance, dimensional stability, and reliability of composite components. Inadequate cure schedules can lead to incomplete curing, excessive shrinkage, residual stresses, deformation, or premature failure, while overly slow curing increases production time and processing costs. Balancing these competing constraints remains a major challenge in composite production.
This work presents an integrated framework that combines empirical cure-kinetics modelling with advanced process simulations to address these challenges. The underlying chemical reactions governing cure progression are characterised experimentally, and the resulting kinetics models describe the evolution of degree of cure as a function of time and temperature. These models are then embedded within finite element simulations capable of predicting cure-induced heat generation, heat transfer, volumetric shrinkage, and the development of residual stresses within moulded and laminated composite structures. Experimental validation using representative thermoset composite parts ensures accurate prediction of thermomechanical behaviour throughout the curing process.
To support optimisation of composite processing, a multi-objective optimiser is incorporated into the framework to navigate trade-offs between conflicting manufacturing goals. Objectives such as minimising cure-cycle duration and shrinkage while maximising degree of cure and part performance are explored simultaneously. The optimiser generates Pareto-optimal cure profiles tailored for composite manufacturing, providing balanced solutions that enhance production efficiency, reduce defects, and ensure high material quality.
This integrated approach offers a robust framework for predictive processing, enabling more efficient design, reduced waste, and improved reliability in composite manufacturing.