Shearography data for deep defect detection and characterization in thick GFRP laminates

Datacite citation style

Nan Tao; Andrei Anisimov; Groves, Roger (2022): Shearography data for deep defect detection and characterization in thick GFRP laminates. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/21674780.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Thick composite materials, e.g. thickness of more than 50 mm, are increasingly used in a wide variety of industries including aerospace and marine sectors. Nevertheless, defect detection and characterization in these materials remain an appealing challenge. The objective of this research aims at improving deep defect characterization in thick composites with shearography. the raw data (phase-shifted speckle interferograms) are available with the metadata to reproduce the experimental results. Additional interactive videos with the variation of the phase induced by the defects are generated in the dataset

History

  • 2022-12-06 first online, published, posted

Publisher

4TU.ResearchData

Format

.avi and .bmp

Funding

  • OPZuid project PROJ-00730
  • EFRO nr. 31B1.0730.

Organizations

TU Delft, Faculty of Aerospace Engineering, Department of Aerospace Structures and Materials

DATA

Files (5)