Imagine baking a complex cake where you can see inside the oven and fix a mistake before the cake is ruined. This technology does that for high-tech carbon fiber parts by spotting errors during the resin filling process. It tells the worker exactly how to fix the problem in real-time so they don't have to throw the whole part in the trash.
Composite manufacturing often suffers from defects that are only discovered after the part is finished, leading to massive material waste and high costs. There is a lack of real-time tools to correct these errors during the infusion process.
A Hybrid Twin platform for curing and infusion simulations, an AI-based Defect Severity Estimation Tool, and a real-time Decision Support System for operators.
If you are an aircraft component manufacturer dealing with high scrap rates of carbon fiber wings — this project developed a real-time monitoring and decision support system that enables first-time-right manufacturing. This reduces the generation of expensive polymer composite waste.
If you are a luxury yacht builder dealing with inconsistent resin infusion in large hulls — this project developed a Hybrid Twin platform that allows for virtual assessment of part performance. This ensures the final product is flawless before it leaves the mold.
If you are a wind turbine blade producer dealing with internal defects in massive composite structures — this project developed an AI-based Defect Severity Estimation Tool. It identifies flaws early and suggests corrective actions to avoid wasting raw materials.
Based on available project data, specific pricing for the end-user is not mentioned, although the project received an EU contribution of EUR 5,612,470 for development.
Yes, the project includes a test bed for experimental validation and 2 industrial use cases to ensure the solution is replicable in real factory settings.
Based on available project data, the project is developing a standardization roadmap to ensure wide impact, but specific licensing terms are not detailed.
The system uses a microservice-based architecture and a digital data space that allows live integration with sensors and existing inspection tools.
The project period runs from 2022-10-01 to 2026-09-30, indicating that full validation and use-case testing are ongoing through 2026.
The consortium is strongly industry-led with a 47% industry ratio, comprising 7 industrial partners and 5 SMEs across 9 countries. This balance, combined with 2 universities and 5 research centers, suggests the project is driven by commercial viability and practical application rather than pure theory.
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