Dataset Open Access

Laser triangulation measurement for AFP monitoring

Sebastian Zambal


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        <foaf:name>Sebastian Zambal</foaf:name>
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            <foaf:name>PROFACTOR GmbH</foaf:name>
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    <dct:title>Laser triangulation measurement for AFP monitoring</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2019</dct:issued>
    <dcat:keyword>Automated Fiber Placement</dcat:keyword>
    <dcat:keyword>Laser Triangulation</dcat:keyword>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2019-08-20</dct:issued>
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    <dct:description>&lt;p&gt;This data set was acquired in the context of EU project ZAero. This project has received funding from the European Union&amp;rsquo;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 HDF5 file with data used for evaluation in a SAMPE 2019 conference paper [1].&lt;/p&gt; &lt;p&gt;The main data in this package is contained in a HDF5 file: zaeroFTD1.h5. There are two entries in this file:&lt;br&gt; /raw: contains the raw laser range data as acuqired during AFP lay-up&lt;br&gt; /seg: contains the manually defined segmentation that corresponds to the laser range data&lt;/p&gt; &lt;p&gt;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):&lt;br&gt; 1 ... gap&lt;br&gt; 2 ... regular tow&lt;br&gt; 3 ... overlap&lt;br&gt; 4 ... fuzzball&lt;/p&gt; &lt;p&gt;This data is mainly intended for testing of algorithms that perform defect detection on laser range images of AFP data.&lt;/p&gt; &lt;p&gt;For more information about the HDF5 format, please visit the HDF5 Group website:&lt;br&gt; https://www.hdfgroup.org/solutions/hdf5/&lt;/p&gt; &lt;p&gt;An example for loading and visualizing the data in Python comes with this data set:&lt;br&gt; readDataExample.py&lt;/p&gt; &lt;p&gt;=====================================&lt;br&gt; REFERENCES&lt;br&gt; =====================================&lt;/p&gt; &lt;p&gt;[1]&lt;br&gt; @inproceedings{Zambal2019_SAMPE}&lt;br&gt; &amp;nbsp; author&amp;nbsp;&amp;nbsp;&amp;nbsp; = {Sebastian Zambal and Christoph Heindl and Christian Eitzinger},&lt;br&gt; &amp;nbsp; title&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; = {Machine Learning for CFRP Quality Control},&lt;br&gt; &amp;nbsp; booktitle = {Conference of the Society for the Advancement of Material and Process Engineering (SAMPE), Nantes, France},&lt;br&gt; &amp;nbsp; year&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; = {2019}&lt;br&gt; }&lt;/p&gt; &lt;p&gt;[2]&lt;br&gt; @inproceedings{Zambal2019_QCAV,&lt;br&gt; &amp;nbsp; author&amp;nbsp;&amp;nbsp;&amp;nbsp; = {Sebastian Zambal and Christoph Heindl and Christian Eitzinger and Josef Scharinger},&lt;br&gt; &amp;nbsp; title&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; = {End-to-End Defect Detection in Automated Fiber Placement Based on Artifcially Generated Data},&lt;br&gt; &amp;nbsp; booktitle = {Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111721G},&lt;br&gt; &amp;nbsp; doi&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; = {DOI: 10.1117/12.2521739},&lt;br&gt; &amp;nbsp; year&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; = {2019}&lt;br&gt; }&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;</dct:description>
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    <dct:title>Zero-defect manufacturing of composite parts in the aerospace industry</dct:title>
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