Dataset Open Access

Laser triangulation measurement for AFP monitoring

Sebastian Zambal

This data set was acquired in the context of EU project ZAero. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 721362. Project duration: 2016/10/01 - 2019/09/30. This data set contains a set of 68 HDF5 files from two different example surface patches of NCF carbon fiber fabrics.

The main data in this package is contained in a HDF5 file: zaeroFTD1.h5. There are two entries in this file:
/raw: contains the raw laser range data as acuqired during AFP lay-up
/seg: contains the manually defined segmentation that corresponds to the laser range data

The data was acquired for preliminary test runs for AFP monitoring in the ZAero project. It contains different regions that correspond to the following labels (as defined in /seg):
1 ... gap
2 ... regular tow
3 ... overlap
4 ... fuzzball

This data is mainly intended for testing of algorithms that perform defect detection on laser range images of AFP data.

For more information about the HDF5 format, please visit the HDF5 Group website:
https://www.hdfgroup.org/solutions/hdf5/

An example for loading and visualizing the data in Python comes with this data set:
readDataExample.py

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REFERENCES
=====================================

[1]
@inproceedings{Zambal2019_SAMPE}
  author    = {Sebastian Zambal and Christoph Heindl and Christian Eitzinger},
  title     = {Machine Learning for CFRP Quality Control},
  booktitle = {Conference of the Society for the Advancement of Material and Process Engineering (SAMPE), Nantes, France},
  year      = {2019}
}

[2]
@inproceedings{Zambal2019_QCAV,
  author    = {Sebastian Zambal and Christoph Heindl and Christian Eitzinger and Josef Scharinger},
  title     = {End-to-End Defect Detection in Automated Fiber Placement Based on Artifcially Generated Data},
  booktitle = {Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111721G},
  doi       = {DOI: 10.1117/12.2521739},
  year      = {2019}
}

 

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