Numerical and experimental analyses of resin infusion manufacturing processes of composite materials
Peng Wang (Lcg-Ensmse, Mpe-Ensmse, Sms-Ensmse), Sylvain Drapier, (Mpe-Ensmse, Sms-Ensmse, Ltds-Ensmse), J\'er\^ome Molimard (Lcg-Ensmse,, Mpe-Ensmse, D2bm-Ensmse, Ifresis-Ensmse, Cis-Ensmse), Alain Vautrin, (Mpe-Ensmse, Sms-Ensmse, Ltds-Ensmse), Jean-Christophe Minni

TL;DR
This paper combines numerical modeling and experimental validation to analyze resin infusion processes in composite manufacturing, aiming to optimize process parameters and improve design accuracy for complex parts.
Contribution
It introduces a validated isothermal numerical model that predicts resin flow and preform deformation during infusion, enhancing understanding and control of the process.
Findings
Numerical predictions match experimental results for filling time and thickness.
Resin flow and preform deformation are effectively monitored during infusion.
Process parameters like temperature influence fiber volume fraction and final part quality.
Abstract
Liquid resin infusion (LRI) processes are promising manufacturing routes to produce large, thick, or complex structural parts. They are based on the resin flow induced, across its thickness, by a pressure applied onto a preform/resin stacking. However, both thickness and fiber volume fraction of the final piece are not well controlled since they result from complex mechanisms which drive the transient mechanical equilibrium leading to the final geometrical configuration. In order to optimize both design and manufacturing parameters, but also to monitor the LRI process, an isothermal numerical model has been developed which describes the mechanical interaction between the deformations of the porous medium and the resin flow during infusion.1, 2 With this numerical model, it is possible to investigate the LRI process of classical industrial part shapes. To validate the numerical model,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
