Summary
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 pre-processing 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.
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 pre-processing 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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More information & hyperlinks
Web resources: | https://cordis.europa.eu/project/id/871875 |
Start date: | 01-01-2020 |
End date: | 31-12-2023 |
Total budget - Public funding: | 5 962 311,00 Euro - 5 089 284,00 Euro |
Cordis data
Original description
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 pre-processing 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.
Status
SIGNEDCall topic
ICT-05-2019Update Date
27-10-2022
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