She / Her
PhD Candidate
Project: Modelling and Simulation of Wet Compression Moulding Process
Linkedin fernanda.blancsoto [at] mail.mcgill.ca (Mail) Publications
Supervisor: Prof. Pascal Hubert
Liquid Composite Moulding (LCM) processes are widely used in aerospace, automotive, and other high-performance industries to manufacture complex fibre-reinforced polymer (FRP) components, valued for their strength-to-weight ratio and durability. As production demands increase, scalable, cost-effective, and automated solutions are essential.
Wet Compression Moulding (WCM) has emerged as a promising alternative to traditional Resin Transfer Moulding (RTM), offering benefits such as reduced cycle times, lower cavity pressures, and simpler equipment by eliminating the injection step. However, WCM faces challenges including void formation, process variability, and control of initial conditions, stemming from the complex interplay between resin flow, thermochemical reactions, fibre compaction, and forming, factors critical to final part quality and performance.
This research aims to investigate the multiphysics phenomena in Wet Compression Moulding (WCM) to optimize the process for producing high-quality parts, while highlighting the key advantages of the technique.
This research will develop numerical simulation tools to model fluid–structure interactions in Wet Compression Moulding. To validate and calibrate the simulations, experimental studies will monitor key process parameters, including flow front progression, pressure, moulding time, and temperature. The mechanical performance of the resulting composites will be evaluated under these conditions.
The research is expected to deliver a validated numerical framework for simulating Wet Compression Moulding, providing insights into the interaction between resin flow, fibre deformation, and thermal behaviour. This framework will enable process optimization for improved part quality and reduced variability, supporting the industrial adoption of WCM through predictive tools and enhanced understanding of critical process parameters.
