The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/21674780

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

Dataset

• Mechanical Engineering
• Aerospace Engineering

Deep defect characterization, finite element method (FEM), glass fiber reinforced polymer (GFRP), shearography, thick composites

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Nan Tao , Andrei Anisimov , Roger Groves

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

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

4TU.ResearchData

.avi and .bmp

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

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