Strain-based NDE for online inspection and prognostics of structures with manufacturing defects

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Strain-based NDE for online inspection and prognostics of structures with manufacturing defects

Host Institutions: University of Southampton

Researcher: Professor Andrew Long

Lead Investigators: Ian Sinclair

Industrial Collaborators: Airbus, BAE Systems, GKN Aerospace, Luxfer, Rolls-Royce

Aims

  • Develop a research concept to detect manufacturing induced defects and their criticality within a production environment.
  • Determine if the component is fit for service, requires repair or should be scrapped if a defect is found

Methodology

  • Integrate Thermoelastic Stress Analysis (TSA) and Digital Image Correlation (DIC) to produce a NDE technique to observe the effect of seeded defects in components produced by resin infusion
  • Develop the means to produce a local multiaxial/complex load to provide a change in strain, to enable TSA and DIC to be used
  • Compare observations from NDE to X-ray CT images
				
								
				
Strain-based NDE for online inspection and prognostics of structures with manufacturing defects
Strain-based NDE for online inspection and prognostics of structures with manufacturing defects
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