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Deep Learning-based delamination assessment of complex composite structures from...

Deep Learning-based delamination assessment of complex composite structures from UGW responses under varying environment 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... ver más

31/01/2026

KU Leuven

176K€

Presupuesto del proyecto: 176K€

Líder del proyecto

KATHOLIEKE UNIVERSITEIT LEUVEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-06-30

HORIZON-MSCA-2022-PF-01-01: MSCA Postdoctoral Fellowships 2022 ExpectedOutcome:

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No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

KU Leuven

175.92K€ | Lider

NATIONAL TECHNICAL UNIVERSIT...

N.D. | Participante

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Duración del proyecto: 31 meses Fecha Inicio: 2023-06-30
Fecha Fin: 2026-01-31

Líder del proyecto

KATHOLIEKE UNIVERSITEIT LEUVEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 176K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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