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 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.