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corrosion-fatigue conditions by integrating multiscale physics-based models combined with mesoscale experimental tests. This research will study the effects of corrosion-induced changes in composition
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
Thermography. This raw dataset is needed to be processed and annotated to train supervised and unsupervised AI models. The research will aim to develop deep learning algorithms for damage classification
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Fibre reinforced composites have excellent in plane strength and stiffness and are being used in increasing quantities in aerospace, sports, automotive and wind turbine blade industries. However
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The research in this doctoral opportunity will develop a failure model that can represent the combined effect of surface and bending failures in gears to perform reliable health prognostics. Lack
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-DED process. Finite element analysis (FEA) is widely used to predict the temperature field during the WA-DED process. Traditional FEA models rely heavily on empirical heat source definitions, such as
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limitations in both measurement and modelling techniques. Current in-process measurement methods are restricted to surface-only monitoring devices (e.g., cameras and pyrometers), which fail to capture
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using learning algorithms as Extreme Learning Machine (ELM) is that training data should cover the entire domain of process parameters to achieve accurate generalization of the trained model to new
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signal processing methods and a modelling environment, aided by unique hardware-in-the loop, to assess the detection and estimation algorithm performance and determine optimal multistatic configurations
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the challenge of forever chemicals in drinking water. The aim of this research is to develop a smart data predictive model that will support utilities’ evidence-based decision-making to improve the resilience and
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for three years. The project focuses on environmental sustainability in grain storage, developing telemetric robotic sensing and predictive modelling to control mycotoxin (Ochratoxin A) risk while reducing