Project description
Silicon photonic sensors for aerospace composites manufacturing
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.
Objective
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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- engineering and technologymaterials engineeringcomposites
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- natural scienceschemical sciencesinorganic chemistrymetalloids
You need to log in or register to use this function
Keywords
Programme(s)
Funding Scheme
IA - Innovation actionCoordinator
106 82 ATHINA
Greece