Summary
The present research proposal aims towards developing a Deep Learning (DL)-based inverse delamination damage assessment
approach in complex industrial composite structures from Ultrasonic Guided Wave (UGW) responses under extreme and varying
operating and environmental conditions (temperature, humidity, pressure). The proposal consists of a number of important
innovative components, such as a) Developing an efficient model for easy incorporation of single and multiple interface delamination
b) Utilizing a mesh-free method to overcome the drawbacks of finite element method c) Modelling accurate wave-damage
interaction under extreme and varying environments d) Constructing a DL-based robust inverse approach to perform effectively
under varying structural complexity and operating environments e) Validating the approach for real-life/ laboratory samples. Meshfree models will provide sufficient flexibility to model geometric complexity and damages besides significant reduction in
computational cost. DL's capability of handling large data sets and predicting optimum output from raw response will provide a
superior approach to predict damages from raw UGW responses. Therefore, this proposal will pave pathways to develop the next
generation of ‘online’, fast and robust delamination assessment tools for industrial complex composite structures under varying
operating environments. This will further enhance European industrial competitiveness and leadership through reducing the
inspection cost by assessing the structural integrity of a complex structure without stopping its normal operations. The Fellow's
expertise in delamination modelling and assessment and the Supervisor's expertise in modelling UGW propagation in complex
structures will create two-way knowledge transfer between them, which will create major scientific, social and economic
advancement in European aviation, energy and civil industries by providing online and accurate diagnostic and prognostic
technologies.
approach in complex industrial composite structures from Ultrasonic Guided Wave (UGW) responses under extreme and varying
operating and environmental conditions (temperature, humidity, pressure). The proposal consists of a number of important
innovative components, such as a) Developing an efficient model for easy incorporation of single and multiple interface delamination
b) Utilizing a mesh-free method to overcome the drawbacks of finite element method c) Modelling accurate wave-damage
interaction under extreme and varying environments d) Constructing a DL-based robust inverse approach to perform effectively
under varying structural complexity and operating environments e) Validating the approach for real-life/ laboratory samples. Meshfree models will provide sufficient flexibility to model geometric complexity and damages besides significant reduction in
computational cost. DL's capability of handling large data sets and predicting optimum output from raw response will provide a
superior approach to predict damages from raw UGW responses. Therefore, this proposal will pave pathways to develop the next
generation of ‘online’, fast and robust delamination assessment tools for industrial complex composite structures under varying
operating environments. This will further enhance European industrial competitiveness and leadership through reducing the
inspection cost by assessing the structural integrity of a complex structure without stopping its normal operations. The Fellow's
expertise in delamination modelling and assessment and the Supervisor's expertise in modelling UGW propagation in complex
structures will create two-way knowledge transfer between them, which will create major scientific, social and economic
advancement in European aviation, energy and civil industries by providing online and accurate diagnostic and prognostic
technologies.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101102294 |
| Start date: | 01-02-2024 |
| End date: | 31-01-2026 |
| Total budget - Public funding: | - 175 920,00 Euro |
Cordis data
Original description
The present research proposal aims towards developing a Deep Learning (DL)-based inverse delamination damage assessmentapproach in complex industrial composite structures from Ultrasonic Guided Wave (UGW) responses under extreme and varying
operating and environmental conditions (temperature, humidity, pressure). The proposal consists of a number of important
innovative components, such as a) Developing an efficient model for easy incorporation of single and multiple interface delamination
b) Utilizing a mesh-free method to overcome the drawbacks of finite element method c) Modelling accurate wave-damage
interaction under extreme and varying environments d) Constructing a DL-based robust inverse approach to perform effectively
under varying structural complexity and operating environments e) Validating the approach for real-life/ laboratory samples. Meshfree models will provide sufficient flexibility to model geometric complexity and damages besides significant reduction in
computational cost. DL's capability of handling large data sets and predicting optimum output from raw response will provide a
superior approach to predict damages from raw UGW responses. Therefore, this proposal will pave pathways to develop the next
generation of ‘online’, fast and robust delamination assessment tools for industrial complex composite structures under varying
operating environments. This will further enhance European industrial competitiveness and leadership through reducing the
inspection cost by assessing the structural integrity of a complex structure without stopping its normal operations. The Fellow's
expertise in delamination modelling and assessment and the Supervisor's expertise in modelling UGW propagation in complex
structures will create two-way knowledge transfer between them, which will create major scientific, social and economic
advancement in European aviation, energy and civil industries by providing online and accurate diagnostic and prognostic
technologies.
Status
SIGNEDCall topic
HORIZON-MSCA-2022-PF-01-01Update Date
31-07-2023
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