Proposal ID: 871875
ICCS project ID: 63111300
Role: Coordinator
Acronym: SEER
Topic: ICT-05-2019
Type of action: IA
Call identifier: H2020-ICT-2018-2020

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

Duration in months: 36
Fixed keyword 1: Photonic integration, photonic integrated circuits
Fixed keyword 2: Photonic devices
Free keywords: Optical sensors, photonic integrated circuits, composite tooling, composite manufacturing, process automation, industry 4.0, aerospace

SEER aims to develop smart self-monitoring composite tools, able to measure process and material parameters and, thus, to provide real-time process control with unprecedented reliability. SEER consortium will achieve this by: 1) developing miniature photonic sensors, 2) embedding those sensors in the tool with through-the-thickness techniques which minimise alteration of the structural integrity of the tool itself and 3) optimising the manufacturing control system through the implementation of a prototype process monitoring, optimisation, and process control unit.
SEER will adopt a multi-sensor approach that will comprise a temperature, a refractive index, and a pressure sensor, operating in the near infrared and all integrated on a miniature photonic integrated circuit (PIC). The SEER solution will be compatible with and optimise existing composite manufacturing methods and its reuse for several resin curing cycles will increase efficiency and save resources. The embedded PIC sensors in a reusable tool will cater perfectly to address preprocessing and will use acquired raw data for process optimisation, using theoretical models and machine learning algorithms, establishing for each tool a link between the sensor data, material state models, process parameters, as well as degradation of the tool. This will allow efficient preventive maintenance of the tool with less effort and provide insight on better tool design. Finally, the acquired data from quality testing of cured parts will be used to optimise the process control ensuring further enhance in the quality yield and will provide with a part quality fingerprint.

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