A “Smart” Self-monitoring composite tool for aerospace composite manufacturing using Silicon photonic multi-sEnsors Embedded using through-thickness Reinforcement techniques
Source: EU Funding & Tenders Portal
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 o
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Participants
Sponsoring Agency | Obfuscated Data |
Company | Obfuscated Data |
Status
Original status | ended |
Taiyo status | Obfuscated Data |
Taiyo last update | 00-00-0000 |
Available timestamps | 00-00-0000 |
Available timestamp type | Obfuscated Data |
Contact
Contact name | Obfuscated Data |
Phone | 0000000000 |
ObfuscatedData@email.com | |
Address | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Description
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. |
Original sub-sector | Obfuscated |
Original Currency | USD |
Original budget | 000000000000000 |
Procurement method | Obfuscated Data |
Budget | 000000000000000 |
Location
Region | Obfuscated |
Country | Obfuscated |
State | Obfuscated Data |
County | Obfuscated |
Location | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Source
Source reliability | High |
Data quality score | 100% |
Source | Obfuscated Data |
URL | obfuscated_data,obfuscateddata.com |
More Details
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