Cargando…

Fast FMCW Terahertz Imaging for In-Process Defect Detection in Press Sleeves for the Paper Industry and Image Evaluation with a Machine Learning Approach

We present a rotational terahertz imaging system for inline nondestructive testing (NDT) of press sleeves for the paper industry during fabrication. Press sleeves often consist of polyurethane (PU) which is deposited by rotational molding on metal barrels and its outer surface mechanically processed...

Descripción completa

Detalles Bibliográficos
Autores principales: Bauer, Maris, Hussung, Raphael, Matheis, Carsten, Reichert, Hermann, Weichenberger, Peter, Beck, Jens, Matuschczyk, Uwe, Jonuscheit, Joachim, Friederich, Fabian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512336/
https://www.ncbi.nlm.nih.gov/pubmed/34640889
http://dx.doi.org/10.3390/s21196569
_version_ 1784582966368796672
author Bauer, Maris
Hussung, Raphael
Matheis, Carsten
Reichert, Hermann
Weichenberger, Peter
Beck, Jens
Matuschczyk, Uwe
Jonuscheit, Joachim
Friederich, Fabian
author_facet Bauer, Maris
Hussung, Raphael
Matheis, Carsten
Reichert, Hermann
Weichenberger, Peter
Beck, Jens
Matuschczyk, Uwe
Jonuscheit, Joachim
Friederich, Fabian
author_sort Bauer, Maris
collection PubMed
description We present a rotational terahertz imaging system for inline nondestructive testing (NDT) of press sleeves for the paper industry during fabrication. Press sleeves often consist of polyurethane (PU) which is deposited by rotational molding on metal barrels and its outer surface mechanically processed in several milling steps afterwards. Due to a stabilizing polyester fiber mesh inlay, small defects can form on the sleeve’s backside already during the initial molding, however, they cannot be visually inspected until the whole production processes is completed. We have developed a fast-scanning frequenc-modulated continuous wave (FMCW) terahertz imaging system, which can be integrated into the manufacturing process to yield high resolution images of the press sleeves and therefore can help to visualize hidden structural defects at an early stage of fabrication. This can save valuable time and resources during the production process. Our terahertz system can record images at 0.3 and 0.5 THz and we achieve data acquisition rates of at least 20 kHz, exploiting the fast rotational speed of the barrels during production to yield sub-millimeter image resolution. The potential of automated defect recognition by a simple machine learning approach for anomaly detection is also demonstrated and discussed.
format Online
Article
Text
id pubmed-8512336
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-85123362021-10-14 Fast FMCW Terahertz Imaging for In-Process Defect Detection in Press Sleeves for the Paper Industry and Image Evaluation with a Machine Learning Approach Bauer, Maris Hussung, Raphael Matheis, Carsten Reichert, Hermann Weichenberger, Peter Beck, Jens Matuschczyk, Uwe Jonuscheit, Joachim Friederich, Fabian Sensors (Basel) Article We present a rotational terahertz imaging system for inline nondestructive testing (NDT) of press sleeves for the paper industry during fabrication. Press sleeves often consist of polyurethane (PU) which is deposited by rotational molding on metal barrels and its outer surface mechanically processed in several milling steps afterwards. Due to a stabilizing polyester fiber mesh inlay, small defects can form on the sleeve’s backside already during the initial molding, however, they cannot be visually inspected until the whole production processes is completed. We have developed a fast-scanning frequenc-modulated continuous wave (FMCW) terahertz imaging system, which can be integrated into the manufacturing process to yield high resolution images of the press sleeves and therefore can help to visualize hidden structural defects at an early stage of fabrication. This can save valuable time and resources during the production process. Our terahertz system can record images at 0.3 and 0.5 THz and we achieve data acquisition rates of at least 20 kHz, exploiting the fast rotational speed of the barrels during production to yield sub-millimeter image resolution. The potential of automated defect recognition by a simple machine learning approach for anomaly detection is also demonstrated and discussed. MDPI 2021-09-30 /pmc/articles/PMC8512336/ /pubmed/34640889 http://dx.doi.org/10.3390/s21196569 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bauer, Maris
Hussung, Raphael
Matheis, Carsten
Reichert, Hermann
Weichenberger, Peter
Beck, Jens
Matuschczyk, Uwe
Jonuscheit, Joachim
Friederich, Fabian
Fast FMCW Terahertz Imaging for In-Process Defect Detection in Press Sleeves for the Paper Industry and Image Evaluation with a Machine Learning Approach
title Fast FMCW Terahertz Imaging for In-Process Defect Detection in Press Sleeves for the Paper Industry and Image Evaluation with a Machine Learning Approach
title_full Fast FMCW Terahertz Imaging for In-Process Defect Detection in Press Sleeves for the Paper Industry and Image Evaluation with a Machine Learning Approach
title_fullStr Fast FMCW Terahertz Imaging for In-Process Defect Detection in Press Sleeves for the Paper Industry and Image Evaluation with a Machine Learning Approach
title_full_unstemmed Fast FMCW Terahertz Imaging for In-Process Defect Detection in Press Sleeves for the Paper Industry and Image Evaluation with a Machine Learning Approach
title_short Fast FMCW Terahertz Imaging for In-Process Defect Detection in Press Sleeves for the Paper Industry and Image Evaluation with a Machine Learning Approach
title_sort fast fmcw terahertz imaging for in-process defect detection in press sleeves for the paper industry and image evaluation with a machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512336/
https://www.ncbi.nlm.nih.gov/pubmed/34640889
http://dx.doi.org/10.3390/s21196569
work_keys_str_mv AT bauermaris fastfmcwterahertzimagingforinprocessdefectdetectioninpresssleevesforthepaperindustryandimageevaluationwithamachinelearningapproach
AT hussungraphael fastfmcwterahertzimagingforinprocessdefectdetectioninpresssleevesforthepaperindustryandimageevaluationwithamachinelearningapproach
AT matheiscarsten fastfmcwterahertzimagingforinprocessdefectdetectioninpresssleevesforthepaperindustryandimageevaluationwithamachinelearningapproach
AT reicherthermann fastfmcwterahertzimagingforinprocessdefectdetectioninpresssleevesforthepaperindustryandimageevaluationwithamachinelearningapproach
AT weichenbergerpeter fastfmcwterahertzimagingforinprocessdefectdetectioninpresssleevesforthepaperindustryandimageevaluationwithamachinelearningapproach
AT beckjens fastfmcwterahertzimagingforinprocessdefectdetectioninpresssleevesforthepaperindustryandimageevaluationwithamachinelearningapproach
AT matuschczykuwe fastfmcwterahertzimagingforinprocessdefectdetectioninpresssleevesforthepaperindustryandimageevaluationwithamachinelearningapproach
AT jonuscheitjoachim fastfmcwterahertzimagingforinprocessdefectdetectioninpresssleevesforthepaperindustryandimageevaluationwithamachinelearningapproach
AT friederichfabian fastfmcwterahertzimagingforinprocessdefectdetectioninpresssleevesforthepaperindustryandimageevaluationwithamachinelearningapproach