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A “Smart” Self-monitoring composite tool for aerospace composite manufacturing using Silicon photonic multi-sEnsors Embedded using through-thickness Reinforcement techniques

Periodic Reporting for period 1 - SEER (A “Smart” Self-monitoring composite tool for aerospace composite manufacturing using Silicon photonic multi-sEnsors Embedded using through-thickness Reinforcement techniques)

Reporting period: 2020-01-01 to 2021-06-30

An increase in airline traffic, coupled with rising fuel costs and strict environmental regulations, is driving the increased use of composite materials in the aerospace industry. Using silicon photonic multi-sensors, the EU-funded SEER project is developing smart self-monitoring composite tools to measure process and material parameters. The aim is to leverage machine learning to provide unprecedented reliability of the cured part while significantly cutting costs through preventive maintenance of the tools. Specifically, the project will develop miniature photonic sensors to embed in the tool with through-the-thickness techniques that minimise alteration of the tool's structural integrity. The sensors will be capable of providing temperature, refractive index and pressure data of the composite part without compromising its structure. It will also provide a part quality fingerprint, ensuring the quality of the part based on the undergone curing process. The SEER solution will be made compatible with existing composite manufacturing and measurement methods.
SEER partners have managed to design, fabricate, optimise the interfaces and package a Temperature Photonic Integrated (PIC) sensor that will be integrated in the remaining composite moulds from the 1st generation of tools. Innovative portable insertion methods and corresponding tools have been developed to facilitate the insertion the PIC sensors into the tools, while the fabrication of the 1st composite tool within SEER has been used to develop and test the Process Monitoring and Control (PMOC) system.
Temperature PIC sensors that can be inserted into composite tools have been developed and optimisation and design on multi-sensors for Refractive index and Pressure has started. Models based on Machine Learning algorithms are being developed based on physical simulations for filling and curing processes, with optimistic results. Ultimately, using SEER’s multi-sensors and tests on manufactured composite parts using SEER tools will results in smart self-monitoring composite moulds.
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