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Super Resolution Laser Thermography using Spatially and Temporally Structured Heating = = Superauflosende Laserthermografie mit raumlich und zeitlich strukturierter Heizung.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Super Resolution Laser Thermography using Spatially and Temporally Structured Heating =/
Reminder of title:	Superauflosende Laserthermografie mit raumlich und zeitlich strukturierter Heizung.
Author:	Ahmadi, Samim.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	173 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
Subject:	Materials science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28747689
ISBN:	9798535578835

Super Resolution Laser Thermography using Spatially and Temporally Structured Heating = = Superauflosende Laserthermografie mit raumlich und zeitlich strukturierter Heizung.
Ahmadi, Samim.

Super Resolution Laser Thermography using Spatially and Temporally Structured Heating =Superauflosende Laserthermografie mit raumlich und zeitlich strukturierter Heizung. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 173 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (D.Engr.)--Technische Universitaet Berlin (Germany), 2021.

This item must not be sold to any third party vendors.

Active thermography is a thermal imaging technique that is mainly used in nondestructive testing. Invisible defects in materials, such as cracks or voids, can be detected by actively heating the surface of the material and using a thermal imaging camera. The diffusive nature of heat propagation causes blurring in the acquired thermal images and thus imposes a spatial resolution limit for closely spaced defects. Conventional thermographic techniques reach their spatial resolution limits at aspect ratios (distance between two defects: depth of defects) of 2:1 to 1:1.To increase the spatial resolution, so-called super resolution methods for laser thermography are developed and studied. The used high-power lasers allow spatially and temporally structured heating. Structured illumination requires multiple measurements to investigate the whole sample surface, but allows a significant increase of the spatial resolution, as already shown in optical microscopy.In addition, super resolution laser thermography, similar to optical super resolution, applies deconvolution algorithms that use the thermal point spread function. The underlying ill-posed inverse problem for defect reconstruction is solved using sparsity regularization in post-processing to promote sparse solutions, since defects usually appear sparsely. Moreover, the used l2,1-regularization favors pixels that are active over multiple measurements ("joint sparsity"). The focus in the dissertation is on blind illumination, where the inverse problem is solved without knowing the position of the illumination. To avoid a manual choice of regularization parameters and to achieve higher convergence rates for in-situ, high-resolution laser thermography, a suitable deep unfolding network is developed and studied within the doctoral thesis.A total of three different test specimens are examined, two of them made of structural steel (S235JR) with surface anomalies, the other one is additively manufactured from stainless steel (316L) and has internal defects. The anomalies/defects to be investigated have aspect ratios ranging from 2:1 to 1:8 and could be resolved using super resolution laser thermography with reconstruction qualities up to 90 %. The work requires multidisciplinary research to realize such a spatial resolution in laser thermography. This multidisciplinarity is composed of laser thermographic experiments, image and signal processing, development of reconstruction algorithms that solve the underlying inverse problem, the application of machine learning to improve convergence, and extensive studies of the associated experimental, model, and regularization parameters.

ISBN: 9798535578835Subjects--Topical Terms:

543314
Materials science.
Subjects--Index Terms:

Super resolution

Super Resolution Laser Thermography using Spatially and Temporally Structured Heating = = Superauflosende Laserthermografie mit raumlich und zeitlich strukturierter Heizung.
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100 1 $a Ahmadi, Samim. $3 3686949
245 1 0 $a Super Resolution Laser Thermography using Spatially and Temporally Structured Heating = $b Superauflosende Laserthermografie mit raumlich und zeitlich strukturierter Heizung.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 173 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisor: Hellwich, Olaf.
502 $a Thesis (D.Engr.)--Technische Universitaet Berlin (Germany), 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Active thermography is a thermal imaging technique that is mainly used in nondestructive testing. Invisible defects in materials, such as cracks or voids, can be detected by actively heating the surface of the material and using a thermal imaging camera. The diffusive nature of heat propagation causes blurring in the acquired thermal images and thus imposes a spatial resolution limit for closely spaced defects. Conventional thermographic techniques reach their spatial resolution limits at aspect ratios (distance between two defects: depth of defects) of 2:1 to 1:1.To increase the spatial resolution, so-called super resolution methods for laser thermography are developed and studied. The used high-power lasers allow spatially and temporally structured heating. Structured illumination requires multiple measurements to investigate the whole sample surface, but allows a significant increase of the spatial resolution, as already shown in optical microscopy.In addition, super resolution laser thermography, similar to optical super resolution, applies deconvolution algorithms that use the thermal point spread function. The underlying ill-posed inverse problem for defect reconstruction is solved using sparsity regularization in post-processing to promote sparse solutions, since defects usually appear sparsely. Moreover, the used l2,1-regularization favors pixels that are active over multiple measurements ("joint sparsity"). The focus in the dissertation is on blind illumination, where the inverse problem is solved without knowing the position of the illumination. To avoid a manual choice of regularization parameters and to achieve higher convergence rates for in-situ, high-resolution laser thermography, a suitable deep unfolding network is developed and studied within the doctoral thesis.A total of three different test specimens are examined, two of them made of structural steel (S235JR) with surface anomalies, the other one is additively manufactured from stainless steel (316L) and has internal defects. The anomalies/defects to be investigated have aspect ratios ranging from 2:1 to 1:8 and could be resolved using super resolution laser thermography with reconstruction qualities up to 90 %. The work requires multidisciplinary research to realize such a spatial resolution in laser thermography. This multidisciplinarity is composed of laser thermographic experiments, image and signal processing, development of reconstruction algorithms that solve the underlying inverse problem, the application of machine learning to improve convergence, and extensive studies of the associated experimental, model, and regularization parameters.
520 $a Die aktive Thermografie ist ein warmebildgebendes Verfahren, das vor allem in der zerstorungsfreien Prufung Anwendung findet. Unsichtbare Defekte in Materialien, wie Risse und Hohlstellen, konnen durch aktives Aufheizen der Materialoberflache mithilfe einer Warmebildkamera detektiert werden. Die diffusive Natur der Warmeausbreitung sorgt fur Unscharfe in den aufgenommenen Warmebildern und stellt damit ein ortliches Auflosungslimit bei dicht beieinanderliegenden Defekten dar. Konventionelle Thermografie-Verfahren erreichen ihre ortlichen Auflosungsgrenzen bei Aspektverhaltnissen (Abstand zwischen zwei Defekten : Tiefe der Defekte) von 2:1 bis 1:1. Um die ortliche Auflosung zu erhohen, werden innerhalb der Doktorarbeit sogenannte Super-Resolution Verfahren fur die Laserthermografie entwickelt und studiert. Die verwendeten Hochleistungslaser erlauben ein strukturiertes Heizen in Ort und Zeit. Die strukturierte Beleuchtung erfordert zwar mehrere Messungen, um die gesamte Probenoberflache zu untersuchen, ermoglicht allerdings eine signifikante Erhohung des ortlichen Auflosungsvermogens, wie bereits in der optischen Mikroskopie gezeigt wurde. Daruber hinaus zeichnet sich die Super-Resolution Laserthermografie, ahnlich wie die optische Super-Resolution, durch Entfaltungsalgorithmen aus, die die thermische Punktspreizfunktion benutzen. Das zugrundeliegende, schlechtgestellte inverse Problem fur die Defektrekonstruktion wird mithilfe einer Sparsity-Regularisierung im Post-Processing gelost, um sparse Losungen zu bevorzugen, da Defekte in der Regel sparse bzw. selten auftreten. Zudem bevorzugt die benutzte L21-Regularisierung Pixel, die uber mehrere Messungen aktiv sind ("joint sparsity"). Der Fokus in der Dissertation richtet sich auf blinde Beleuchtung, bei der das inverse Problem gelost wird ohne den Ort der Beleuchtung zu kennen. Um eine manuelle Wahl der Regularisierungsparameter zu umgehen und hohere Konvergenzraten fur in-situ, hochauflosende Laserthermografie zu erreichen, wird ein geeignetes Deep Unfolding Netzwerk innerhalb der Doktorarbeit entwickelt und studiert. Insgesamt werden drei verschiedene Prufkorper untersucht, zwei davon aus Baustahl (S235JR) mit Oberflachenanomalien, der andere ist additiv aus Edelstahl (316L) gefertigt und weist interne Defekte auf. Die zu analysierenden Anomalien/ Defekte haben Aspektverhaltnisse von 2:1 bis 1:8 und konnen durch Super-Resolution Laserthermografie mit Rekonstruktionsqualitaten bis zu 90 % aufgelost werden. Die Arbeit erfordert multidisziplinare Forschung, um solch ein ortliches Auflosungsvermogen in der Laserthermografie realisieren zu konnen. Diese setzt sich zusammen aus laserthermografischen Experimenten, Bild- und Signalverarbeitung, Entwicklung von Rekonstruktionsalgorithmen, die das zugrundeliegende inverse Problem losen, die Anwendung von Machine Learning zur Verbesserung der Konvergenz sowie umfangreiche Studien zu den damit verbundenen experimentellen, Modell- und Regularisierungsparametern.
590 $a School code: 1119.
650 4 $a Materials science. $3 543314
650 4 $a Thermodynamics. $3 517304
650 4 $a Energy. $3 876794
650 4 $a Computer science. $3 523869
650 4 $a Artificial intelligence. $3 516317
650 4 $a Electrical engineering. $3 649834
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650 4 $a Stainless steel. $3 3560388
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653 $a Super resolution
653 $a Laser thermography
653 $a Heating
653 $a Active thermography
653 $a Thermal imaging
653 $a Nondestructive testing
653 $a Defects
653 $a Additive manufacturing
653 $a Machine learning
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710 2 $a Technische Universitaet Berlin (Germany). $3 3480527
773 0 $t Dissertations Abstracts International $g 83-02B.
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791 $a D.Engr.
792 $a 2021
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28747689