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Dataset for surface and internal damage after impact on CFRP laminates.

Saki Hasebe1, Ryo Higuchi1, Tomohiro Yokozeki1

  • 1Department of Aeronautics and Astronautics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656 Japan.

Data in Brief
|July 22, 2022
PubMed
Summary

This study presents a dataset for predicting internal damage in carbon fiber reinforced polymer (CFRP) structures from surface damage images using machine learning. It aids in understanding barely visible impact damage in CFRP materials.

Keywords:
BVIDMachine learningNon-destructive testingThermoset CFRP

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Data Science

Background:

  • Foreign object impact on carbon fiber reinforced polymer (CFRP) structures, like aircraft, can cause both visible and internal damage.
  • Internal damage, such as matrix cracks and delamination, significantly reduces compressive strength in CFRP laminates.
  • The relationship between external surface damage and internal defects in CFRP is often difficult to ascertain, especially for barely visible impact damage.

Purpose of the Study:

  • To develop a dataset for predicting impact information solely from surface damage profiles of CFRP.
  • To facilitate the application of machine learning in analyzing impact damage in CFRP structures.
  • To provide valuable data for researchers and engineers studying CFRP impact behavior and data science applications.

Main Methods:

  • Low-velocity impact testing was performed on CFRP laminates under various conditions.
  • Three types of data were collected: surface damage images (png), surface depth contour images (png), and internal damage images from ultrasound C-scanning (jpg).
  • The dataset is structured for machine learning model training to predict impact characteristics from surface data.

Main Results:

  • A comprehensive dataset containing correlated surface and internal damage information from CFRP impact tests was created.
  • The dataset includes visual and depth data of surface damage alongside corresponding internal damage scans.
  • This data enables the investigation of correlations between observable surface features and subsurface delaminations or cracks.

Conclusions:

  • The prepared dataset is crucial for advancing machine learning-based non-destructive evaluation techniques for CFRP.
  • It offers a valuable resource for predicting internal damage in CFRP structures, improving safety and maintenance protocols.
  • This work bridges the gap between experimental impact testing and data-driven analysis in materials science.