Evaluating conventional and deep learning segmentation for fast X-ray CT porosity measurements of polymer laser sintered AM parts

Laser sintering is evolving towards a genuine manufacturing technique for volume production and mass customized products. However, variability in part quality has to be reduced further to enable its use for high demanding and critical end-use applications. High volume and mass customized manufacturi...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Simon Bellens [verfasserIn] Gabriel M. Probst [verfasserIn] Michel Janssens [verfasserIn] Patrick Vandewalle [verfasserIn] Wim Dewulf [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Additive manufacturing Laser sintering In-line quality control X-ray computed tomography Denoising Segmentation

Übergeordnetes Werk:	In: Polymer Testing - Elsevier, 2021, 110(2022), Seite 107540-
Übergeordnetes Werk:	volume:110 ; year:2022 ; pages:107540-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.polymertesting.2022.107540

Katalog-ID:	DOAJ045051879

Internformat


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allfields_unstemmed	10.1016/j.polymertesting.2022.107540 doi (DE-627)DOAJ045051879 (DE-599)DOAJ5b002be9412140f6b7c5533d05652a52 DE-627 ger DE-627 rakwb eng TP1080-1185 Simon Bellens verfasserin aut Evaluating conventional and deep learning segmentation for fast X-ray CT porosity measurements of polymer laser sintered AM parts 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Laser sintering is evolving towards a genuine manufacturing technique for volume production and mass customized products. However, variability in part quality has to be reduced further to enable its use for high demanding and critical end-use applications. High volume and mass customized manufacturing impose the need for a fast and flexible measurement instrument, to automatically assess the overall part quality, which is currently not available for the AM industry. XCT has shown to be an effective tool to measure the part quality, but the large acquisition time still obstructs the use of XCT for in-line quality inspections of laser sintered parts. Altering the XCT settings to decrease the total acquisition time influences the SNR and CNR of the reconstruction, introduces artefacts and directly influences the segmentation quality and feature analyses. To minimize the influence of the deteriorated image quality, deep learning segmentation algorithms are evaluated and compared with conventional segmentation and denoising algorithms on low-quality XCT scans with reduced acquisition times. The segmentation quality is quantitatively investigated with the Jaccard index, probability of detection, pore size distributions and porosity values and a qualitative comparison is provided. An improved segmentation for low-quality XCT scans is obtained by using deep learning segmentation algorithms while preserving a high generalization of the segmentation algorithm on low-quality XCT scans with a wide SNR and CNR range. Additive manufacturing Laser sintering In-line quality control X-ray computed tomography Denoising Segmentation Polymers and polymer manufacture Gabriel M. Probst verfasserin aut Michel Janssens verfasserin aut Patrick Vandewalle verfasserin aut Wim Dewulf verfasserin aut In Polymer Testing Elsevier, 2021 110(2022), Seite 107540- (DE-627)320530280 (DE-600)2015673-X 18732348 nnns volume:110 year:2022 pages:107540- https://doi.org/10.1016/j.polymertesting.2022.107540 kostenfrei https://doaj.org/article/5b002be9412140f6b7c5533d05652a52 kostenfrei http://www.sciencedirect.com/science/article/pii/S0142941822000654 kostenfrei https://doaj.org/toc/0142-9418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 110 2022 107540-
allfieldsGer	10.1016/j.polymertesting.2022.107540 doi (DE-627)DOAJ045051879 (DE-599)DOAJ5b002be9412140f6b7c5533d05652a52 DE-627 ger DE-627 rakwb eng TP1080-1185 Simon Bellens verfasserin aut Evaluating conventional and deep learning segmentation for fast X-ray CT porosity measurements of polymer laser sintered AM parts 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Laser sintering is evolving towards a genuine manufacturing technique for volume production and mass customized products. However, variability in part quality has to be reduced further to enable its use for high demanding and critical end-use applications. High volume and mass customized manufacturing impose the need for a fast and flexible measurement instrument, to automatically assess the overall part quality, which is currently not available for the AM industry. XCT has shown to be an effective tool to measure the part quality, but the large acquisition time still obstructs the use of XCT for in-line quality inspections of laser sintered parts. Altering the XCT settings to decrease the total acquisition time influences the SNR and CNR of the reconstruction, introduces artefacts and directly influences the segmentation quality and feature analyses. To minimize the influence of the deteriorated image quality, deep learning segmentation algorithms are evaluated and compared with conventional segmentation and denoising algorithms on low-quality XCT scans with reduced acquisition times. The segmentation quality is quantitatively investigated with the Jaccard index, probability of detection, pore size distributions and porosity values and a qualitative comparison is provided. An improved segmentation for low-quality XCT scans is obtained by using deep learning segmentation algorithms while preserving a high generalization of the segmentation algorithm on low-quality XCT scans with a wide SNR and CNR range. Additive manufacturing Laser sintering In-line quality control X-ray computed tomography Denoising Segmentation Polymers and polymer manufacture Gabriel M. Probst verfasserin aut Michel Janssens verfasserin aut Patrick Vandewalle verfasserin aut Wim Dewulf verfasserin aut In Polymer Testing Elsevier, 2021 110(2022), Seite 107540- (DE-627)320530280 (DE-600)2015673-X 18732348 nnns volume:110 year:2022 pages:107540- https://doi.org/10.1016/j.polymertesting.2022.107540 kostenfrei https://doaj.org/article/5b002be9412140f6b7c5533d05652a52 kostenfrei http://www.sciencedirect.com/science/article/pii/S0142941822000654 kostenfrei https://doaj.org/toc/0142-9418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 110 2022 107540-
allfieldsSound	10.1016/j.polymertesting.2022.107540 doi (DE-627)DOAJ045051879 (DE-599)DOAJ5b002be9412140f6b7c5533d05652a52 DE-627 ger DE-627 rakwb eng TP1080-1185 Simon Bellens verfasserin aut Evaluating conventional and deep learning segmentation for fast X-ray CT porosity measurements of polymer laser sintered AM parts 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Laser sintering is evolving towards a genuine manufacturing technique for volume production and mass customized products. However, variability in part quality has to be reduced further to enable its use for high demanding and critical end-use applications. High volume and mass customized manufacturing impose the need for a fast and flexible measurement instrument, to automatically assess the overall part quality, which is currently not available for the AM industry. XCT has shown to be an effective tool to measure the part quality, but the large acquisition time still obstructs the use of XCT for in-line quality inspections of laser sintered parts. Altering the XCT settings to decrease the total acquisition time influences the SNR and CNR of the reconstruction, introduces artefacts and directly influences the segmentation quality and feature analyses. To minimize the influence of the deteriorated image quality, deep learning segmentation algorithms are evaluated and compared with conventional segmentation and denoising algorithms on low-quality XCT scans with reduced acquisition times. The segmentation quality is quantitatively investigated with the Jaccard index, probability of detection, pore size distributions and porosity values and a qualitative comparison is provided. An improved segmentation for low-quality XCT scans is obtained by using deep learning segmentation algorithms while preserving a high generalization of the segmentation algorithm on low-quality XCT scans with a wide SNR and CNR range. Additive manufacturing Laser sintering In-line quality control X-ray computed tomography Denoising Segmentation Polymers and polymer manufacture Gabriel M. Probst verfasserin aut Michel Janssens verfasserin aut Patrick Vandewalle verfasserin aut Wim Dewulf verfasserin aut In Polymer Testing Elsevier, 2021 110(2022), Seite 107540- (DE-627)320530280 (DE-600)2015673-X 18732348 nnns volume:110 year:2022 pages:107540- https://doi.org/10.1016/j.polymertesting.2022.107540 kostenfrei https://doaj.org/article/5b002be9412140f6b7c5533d05652a52 kostenfrei http://www.sciencedirect.com/science/article/pii/S0142941822000654 kostenfrei https://doaj.org/toc/0142-9418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 110 2022 107540-
language	English
source	In Polymer Testing 110(2022), Seite 107540- volume:110 year:2022 pages:107540-
sourceStr	In Polymer Testing 110(2022), Seite 107540- volume:110 year:2022 pages:107540-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Additive manufacturing Laser sintering In-line quality control X-ray computed tomography Denoising Segmentation Polymers and polymer manufacture
isfreeaccess_bool	true
container_title	Polymer Testing
authorswithroles_txt_mv	Simon Bellens @@aut@@ Gabriel M. Probst @@aut@@ Michel Janssens @@aut@@ Patrick Vandewalle @@aut@@ Wim Dewulf @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	320530280
id	DOAJ045051879
language_de	englisch
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callnumber-first	T - Technology
author	Simon Bellens
spellingShingle	Simon Bellens misc TP1080-1185 misc Additive manufacturing misc Laser sintering misc In-line quality control misc X-ray computed tomography misc Denoising misc Segmentation misc Polymers and polymer manufacture Evaluating conventional and deep learning segmentation for fast X-ray CT porosity measurements of polymer laser sintered AM parts
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topic_title	TP1080-1185 Evaluating conventional and deep learning segmentation for fast X-ray CT porosity measurements of polymer laser sintered AM parts Additive manufacturing Laser sintering In-line quality control X-ray computed tomography Denoising Segmentation
topic	misc TP1080-1185 misc Additive manufacturing misc Laser sintering misc In-line quality control misc X-ray computed tomography misc Denoising misc Segmentation misc Polymers and polymer manufacture
topic_unstemmed	misc TP1080-1185 misc Additive manufacturing misc Laser sintering misc In-line quality control misc X-ray computed tomography misc Denoising misc Segmentation misc Polymers and polymer manufacture
topic_browse	misc TP1080-1185 misc Additive manufacturing misc Laser sintering misc In-line quality control misc X-ray computed tomography misc Denoising misc Segmentation misc Polymers and polymer manufacture
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title	Evaluating conventional and deep learning segmentation for fast X-ray CT porosity measurements of polymer laser sintered AM parts
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title_full	Evaluating conventional and deep learning segmentation for fast X-ray CT porosity measurements of polymer laser sintered AM parts
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journal	Polymer Testing
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title_sort	evaluating conventional and deep learning segmentation for fast x-ray ct porosity measurements of polymer laser sintered am parts
callnumber	TP1080-1185
title_auth	Evaluating conventional and deep learning segmentation for fast X-ray CT porosity measurements of polymer laser sintered AM parts
abstract	Laser sintering is evolving towards a genuine manufacturing technique for volume production and mass customized products. However, variability in part quality has to be reduced further to enable its use for high demanding and critical end-use applications. High volume and mass customized manufacturing impose the need for a fast and flexible measurement instrument, to automatically assess the overall part quality, which is currently not available for the AM industry. XCT has shown to be an effective tool to measure the part quality, but the large acquisition time still obstructs the use of XCT for in-line quality inspections of laser sintered parts. Altering the XCT settings to decrease the total acquisition time influences the SNR and CNR of the reconstruction, introduces artefacts and directly influences the segmentation quality and feature analyses. To minimize the influence of the deteriorated image quality, deep learning segmentation algorithms are evaluated and compared with conventional segmentation and denoising algorithms on low-quality XCT scans with reduced acquisition times. The segmentation quality is quantitatively investigated with the Jaccard index, probability of detection, pore size distributions and porosity values and a qualitative comparison is provided. An improved segmentation for low-quality XCT scans is obtained by using deep learning segmentation algorithms while preserving a high generalization of the segmentation algorithm on low-quality XCT scans with a wide SNR and CNR range.
abstractGer	Laser sintering is evolving towards a genuine manufacturing technique for volume production and mass customized products. However, variability in part quality has to be reduced further to enable its use for high demanding and critical end-use applications. High volume and mass customized manufacturing impose the need for a fast and flexible measurement instrument, to automatically assess the overall part quality, which is currently not available for the AM industry. XCT has shown to be an effective tool to measure the part quality, but the large acquisition time still obstructs the use of XCT for in-line quality inspections of laser sintered parts. Altering the XCT settings to decrease the total acquisition time influences the SNR and CNR of the reconstruction, introduces artefacts and directly influences the segmentation quality and feature analyses. To minimize the influence of the deteriorated image quality, deep learning segmentation algorithms are evaluated and compared with conventional segmentation and denoising algorithms on low-quality XCT scans with reduced acquisition times. The segmentation quality is quantitatively investigated with the Jaccard index, probability of detection, pore size distributions and porosity values and a qualitative comparison is provided. An improved segmentation for low-quality XCT scans is obtained by using deep learning segmentation algorithms while preserving a high generalization of the segmentation algorithm on low-quality XCT scans with a wide SNR and CNR range.
abstract_unstemmed	Laser sintering is evolving towards a genuine manufacturing technique for volume production and mass customized products. However, variability in part quality has to be reduced further to enable its use for high demanding and critical end-use applications. High volume and mass customized manufacturing impose the need for a fast and flexible measurement instrument, to automatically assess the overall part quality, which is currently not available for the AM industry. XCT has shown to be an effective tool to measure the part quality, but the large acquisition time still obstructs the use of XCT for in-line quality inspections of laser sintered parts. Altering the XCT settings to decrease the total acquisition time influences the SNR and CNR of the reconstruction, introduces artefacts and directly influences the segmentation quality and feature analyses. To minimize the influence of the deteriorated image quality, deep learning segmentation algorithms are evaluated and compared with conventional segmentation and denoising algorithms on low-quality XCT scans with reduced acquisition times. The segmentation quality is quantitatively investigated with the Jaccard index, probability of detection, pore size distributions and porosity values and a qualitative comparison is provided. An improved segmentation for low-quality XCT scans is obtained by using deep learning segmentation algorithms while preserving a high generalization of the segmentation algorithm on low-quality XCT scans with a wide SNR and CNR range.
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title_short	Evaluating conventional and deep learning segmentation for fast X-ray CT porosity measurements of polymer laser sintered AM parts
url	https://doi.org/10.1016/j.polymertesting.2022.107540 https://doaj.org/article/5b002be9412140f6b7c5533d05652a52 http://www.sciencedirect.com/science/article/pii/S0142941822000654 https://doaj.org/toc/0142-9418
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author2	Gabriel M. Probst Michel Janssens Patrick Vandewalle Wim Dewulf
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doi_str	10.1016/j.polymertesting.2022.107540
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up_date	2024-07-04T01:28:45.413Z
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Evaluating conventional and deep learning segmentation for fast X-ray CT porosity measurements of polymer laser sintered AM parts

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