Manuel Laspalas/Francisco Serrano (ITA) — Meso-scale void prediction during RTM mold filling

Don't miss any Success Story following us on X and LinkedIn! 
@RES_HPC   RES - Red Española de Supercomputación  @res-icts.bsky.social

Check this Success Story at our LinkedIn: Meso-scale void prediction during RTM mold filling

✈️ What if a tiny air pocket could make an aircraft component fail, but we could predict it before manufacturing it?✈️

Composite materials combine different substances to achieve properties that none of them could reach on their own and are essential in areas like aeronautics where strength and reliability are non-negotiable. Tiny air pockets (voids) can form inside the material when manufacturing it, and their location, size and quantity can greatly reduce the strength and durability of the final component. 

The team led by Manuel Laspalas and Francisco Serrano Alcalde (ITA · Instituto Tecnológico de Aragón) tackled this challenge with computational simulations on #Agustina at Instituto de Biocomputacion y Fisica de Sistemas Complejos - BIFI

🔎𝗪𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗳𝗼𝘂𝗻𝗱?🔎 

🔹 By zooming into the spaces between fibres, they discovered that the specific material they tested had much lower porosity than woven fabrics
🔹 The simulations established a relationship between void formation, material and process properties across a wide range of conditions.
🔹 Based on these results, they developed an AI model that predicts void formation for any combination of materials and process properties

👇 Swipe and discover more about the project!