Deep learning-based automated 3D inspection of helical gears using voxelized CAD models and 3D convolutional autoencoders

The automated inspection of complex freeform components, such as helical gears, is a persistent challenge in advanced manufacturing due to their intricate geometries and strict precision requirements. Conventional inspection methods, such as those using coordinate measuring machines or optical techn...

Full description

Saved in:

Bibliographic Details
Published in:	International journal of advanced manufacturing technology Vol. 141; no. 7-8; pp. 3695 - 3715
Main Authors:	Selloum, Rabia, Ameddah, Hacene, Brioua, Mourad
Format:	Journal Article
Language:	English
Published:	London Springer London 01.12.2025 Springer Nature B.V
Subjects:	Accuracy Algorithms Automation CAD CAE) and Design Computer aided design Computer-Aided Engineering (CAD Coordinate measuring machines Datasets Deep learning Defects Engineering Helical gears Industrial and Production Engineering Industrial applications Inspection Inspections Machine learning Manufacturing Measurement techniques Mechanical Engineering Media Management Neural networks Noise sensitivity Noise tolerance Optics Optimization Original Article Perturbation Product reliability Deep learning Helical gear inspection Precision manufacturing Convolutional autoencoder Anomaly detection Non-Destructive testing
ISSN:	0268-3768, 1433-3015
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	The automated inspection of complex freeform components, such as helical gears, is a persistent challenge in advanced manufacturing due to their intricate geometries and strict precision requirements. Conventional inspection methods, such as those using coordinate measuring machines or optical techniques, are often time-consuming and lack adaptability to subtle deviations. Recent deep learning approaches show promise but are typically limited to point-based or scan-to-scan comparisons, which remain sensitive to noise and alignment errors. We propose a voxel-based 3D inspection framework that integrates an XGBoost-guided perturbation model with a 3D convolutional autoencoder (3D CNN-AE). CAD-derived gear models are systematically perturbed with controlled Gaussian deformations to emulate tolerances, defects, and sensor noise, then voxelized for autoencoder training. This enables robust learning of nominal gear geometry distributions. Extensive experiments conducted against PointNet++, a Variational Autoencoder, and a GAN-based reconstruction model demonstrate that our method consistently achieves superior performance across various metrics, including PSNR, SSIM, accuracy, precision, recall, and F1-score. The results highlight the potential of voxel-based learning with data-driven perturbation for scalable and high-accuracy inspection in industrial applications.
AbstractList	The automated inspection of complex freeform components, such as helical gears, is a persistent challenge in advanced manufacturing due to their intricate geometries and strict precision requirements. Conventional inspection methods, such as those using coordinate measuring machines or optical techniques, are often time-consuming and lack adaptability to subtle deviations. Recent deep learning approaches show promise but are typically limited to point-based or scan-to-scan comparisons, which remain sensitive to noise and alignment errors. We propose a voxel-based 3D inspection framework that integrates an XGBoost-guided perturbation model with a 3D convolutional autoencoder (3D CNN-AE). CAD-derived gear models are systematically perturbed with controlled Gaussian deformations to emulate tolerances, defects, and sensor noise, then voxelized for autoencoder training. This enables robust learning of nominal gear geometry distributions. Extensive experiments conducted against PointNet++, a Variational Autoencoder, and a GAN-based reconstruction model demonstrate that our method consistently achieves superior performance across various metrics, including PSNR, SSIM, accuracy, precision, recall, and F1-score. The results highlight the potential of voxel-based learning with data-driven perturbation for scalable and high-accuracy inspection in industrial applications.
Author	Ameddah, Hacene Selloum, Rabia Brioua, Mourad
Author_xml	– sequence: 1 givenname: Rabia orcidid: 0009-0004-5048-7774 surname: Selloum fullname: Selloum, Rabia email: r.selloum@univ-batna2.dz organization: Laboratory of Innovation in Construction, Ecodesign, and Seismic Engineering, Department of Mechanical Engineering, Faculty of Technology, University of Batna 2 – Mostefa Ben Boulaïd – sequence: 2 givenname: Hacene surname: Ameddah fullname: Ameddah, Hacene organization: Laboratory of Innovation in Construction, Ecodesign, and Seismic Engineering, Department of Mechanical Engineering, Faculty of Technology, University of Batna 2 – Mostefa Ben Boulaïd – sequence: 3 givenname: Mourad surname: Brioua fullname: Brioua, Mourad organization: Laboratory of Innovation in Construction, Ecodesign, and Seismic Engineering, Department of Mechanical Engineering, Faculty of Technology, University of Batna 2 – Mostefa Ben Boulaïd
BookMark	eNp9kF1LwzAUhoMouE3_gFcBr6NJujbt5dj8goE3eh3S9GR2dElN2mH99aar4J1X53B4npfDO0fn1llA6IbRO0apuA-UMkEJ5SlhWV5wMpyhGVsmCUkoS8_RjPIsJ4nI8ks0D2Ef8SyCMzRsAFrcgPK2tjtSqgAVVn3nDqqLW7LBtQ0t6K52FjuDP6CptWrwLhoB9yFK-Oi-4vU74uvVBh9cBU3Ayp5s7ezRNf2oR2sMBqsj4cMVujCqCXD9Oxfo_fHhbf1Mtq9PL-vVlmgueEcY02kuRElpBabgkEFeVUwVLGVG55rTKk-VYArMsixgaaq0KJg2JRQmzTLBkwW6nXJb7z57CJ3cu97Hb4JMuMhGRtBI8YnS3oXgwcjW1wflB8moHCuWU8UyVixPFcshSskkhQjbHfi_6H-sHy5Qgug
Cites_doi	10.1109/WSC63780.2024.10838928 10.1109/SEB4SDG60871.2024.10629997 10.1109/GCWOT53057.2022.9772891 10.1016/j.autcon.2021.103675 10.1007/s11668-023-01695-8 10.1007/s11071-024-09465-3 10.1609/aaai.v38i2.27929 10.1016/j.ins.2016.07.030 10.3390/computers11060100 10.36948/ijfmr.2024.v06i06.33087 10.48550/arXiv.1907.03739 10.1007/s40436-014-0059-0 10.48550/arXiv.2503.18283 10.1088/1757-899X/1258/1/012022 10.48550/arXiv.1312.6114 10.1051/metal/2025032 10.1007/978-3-030-86446-0_38 10.1016/j.tws.2021.107540 10.1609/aaai.v38i3.28018 10.48084/etasr.8619 10.1016/j.swevo.2017.04.006 10.17973/MMSJ.2024_11_2024096 10.1109/AIIPCC57291.2022.00086 10.3390/s24144660 10.48550/arXiv.1610.07584 10.1088/2631-8695/ac3e13 10.1016/j.rcim.2019.101843 10.1007/s00170-024-13827-x 10.1016/j.rcim.2023.102687 10.1016/j.mfglet.2024.09.159 10.1109/TPAMI.2022.3194555 10.5445/IR/1000129520 10.3390/s21248480 10.48550/arXiv.1909.12037 10.1002/9781119932475 10.48550/arXiv.1512.03012 10.48550/arXiv.1706.02413 10.1109/TIM.2024.3480198 10.1016/j.ress.2014.09.018 10.1109/ACCESS.2025.3525567 10.15282/ijame.18.2.2021.02.0658 10.1007/978-3-030-52071-7_20 10.3390/machines10090779 10.1016/j.jmsy.2020.05.008 10.48550/arXiv.2203.10314 10.1007/s00170-010-2684-5 10.1109/ACCESS.2023.3271748 10.1109/TIM.2025.3571125 10.1109/CVPR.2015.7298801 10.3390/s23208541
ContentType	Journal Article
Copyright	The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.
Copyright_xml	– notice: The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.
DBID	AAYXX CITATION
DOI	10.1007/s00170-025-16892-y
DatabaseName	CrossRef
DatabaseTitle	CrossRef
DatabaseTitleList
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1433-3015
EndPage	3715
ExternalDocumentID	10_1007_s00170_025_16892_y
GrantInformation_xml	– fundername: Laboratory of Innovation in Construction, Eco-design, and Seismic Engineering (LICEGS), University Mustafa Ben Boulaid of Batna 2, Algeria grantid: A11N01UN050220190003
GroupedDBID	-XW -XX -Y2 -~C .86 .VR 06D 0R~ 0VY 123 1N0 1SB 203 28- 29J 29~ 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6NX 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X 9M8 AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAPKM AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO ABAKF ABBBX ABBRH ABBXA ABDBE ABDBF ABDZT ABECU ABFSG ABFTD ABFTV ABHQN ABJCF ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABRTQ ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSTC ACUHS ACZOJ ADHHG ADHIR ADHKG ADKNI ADKPE ADMLS ADQRH ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AETLH AEVLU AEXYK AEZWR AFBBN AFDZB AFEXP AFFHD AFGCZ AFHIU AFKRA AFLOW AFOHR AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGQPQ AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHPBZ AHSBF AHWEU AHYZX AIAKS AIGIU AIIXL AILAN AITGF AIXLP AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARCEE ARMRJ ASPBG ATHPR AVWKF AXYYD AYFIA AYJHY AZFZN B-. B0M BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EAD EAP EAS EBLON EBS EIOEI EJD EMK EPL ESBYG ESX FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW L6V LAS LLZTM M4Y M7S MA- ML~ N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P9P PF0 PHGZM PHGZT PQGLB PT4 PT5 PTHSS QOK QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCLPG SCV SDH SDM SEG SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TN5 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z8Z ZMTXR ZY4 ~8M ~A9 ~EX AAYXX CITATION
ID	FETCH-LOGICAL-c272t-11c5877b00def92e6e8dd1a9151fc8c20d85a71aef4b9e4fd5991cfbe9f566723
IEDL.DBID	RSV
ISICitedReferencesCount	0
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001610273800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0268-3768
IngestDate	Mon Dec 01 05:11:12 EST 2025 Sat Nov 29 06:58:06 EST 2025 Sun Nov 30 01:10:37 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	7-8
Keywords	Deep learning Helical gear inspection Precision manufacturing Convolutional autoencoder Anomaly detection Non-Destructive testing
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c272t-11c5877b00def92e6e8dd1a9151fc8c20d85a71aef4b9e4fd5991cfbe9f566723
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0009-0004-5048-7774
PQID	3276667270
PQPubID	2044010
PageCount	21
ParticipantIDs	proquest_journals_3276667270 crossref_primary_10_1007_s00170_025_16892_y springer_journals_10_1007_s00170_025_16892_y
PublicationCentury	2000
PublicationDate	20251200
PublicationDateYYYYMMDD	2025-12-01
PublicationDate_xml	– month: 12 year: 2025 text: 20251200
PublicationDecade	2020
PublicationPlace	London
PublicationPlace_xml	– name: London – name: Heidelberg
PublicationTitle	International journal of advanced manufacturing technology
PublicationTitleAbbrev	Int J Adv Manuf Technol
PublicationYear	2025
Publisher	Springer London Springer Nature B.V
Publisher_xml	– name: Springer London – name: Springer Nature B.V
References	F Xie (16892_CR5) 2024; 133 Z Shi (16892_CR40) 2024; 24 16892_CR17 A Allam (16892_CR37) 2021; 21 16892_CR15 16892_CR14 16892_CR13 16892_CR11 T Idzik (16892_CR38) 2023; 23 16892_CR10 O Chamberland (16892_CR16) 2023; 23 MF Ercan (16892_CR7) 2022; 11 R Ascione (16892_CR6) 2010; 51 16892_CR49 16892_CR48 16892_CR47 16892_CR46 16892_CR45 16892_CR42 16892_CR52 16892_CR51 Y Xu (16892_CR43) 2021; 126 16892_CR50 R Cohen (16892_CR12) 2024; 112 M Prunella (16892_CR18) 2023; 11 16892_CR1 16892_CR36 16892_CR35 16892_CR34 T Li (16892_CR31) 2017; 36 16892_CR33 M Chagas Moura (16892_CR23) 2015; 133 16892_CR8 16892_CR41 16892_CR9 Y Liu (16892_CR26) 2020; 56 L Zhu (16892_CR30) 2021; 162 16892_CR4 16892_CR2 16892_CR3 H Du (16892_CR19) 2024; 38 16892_CR28 Q Yu (16892_CR22) 2014; 2 16892_CR27 16892_CR25 16892_CR24 F Cosco (16892_CR29) 2022; 10 Y Huang (16892_CR44) 2023; 37 16892_CR21 16892_CR20 R-S Mei (16892_CR39) 2024; 41 T Li (16892_CR32) 2016; 367
References_xml	– ident: 16892_CR28 doi: 10.1109/WSC63780.2024.10838928 – ident: 16892_CR4 doi: 10.1109/SEB4SDG60871.2024.10629997 – ident: 16892_CR17 doi: 10.1109/GCWOT53057.2022.9772891 – volume: 126 year: 2021 ident: 16892_CR43 publication-title: Autom Constr doi: 10.1016/j.autcon.2021.103675 – ident: 16892_CR13 doi: 10.1109/SEB4SDG60871.2024.10629997 – volume: 23 start-page: 1633 issue: 4 year: 2023 ident: 16892_CR16 publication-title: J Fail Anal Prev doi: 10.1007/s11668-023-01695-8 – volume: 112 start-page: 8089 issue: 10 year: 2024 ident: 16892_CR12 publication-title: Nonlinear Dyn doi: 10.1007/s11071-024-09465-3 – volume: 38 start-page: 1626 year: 2024 ident: 16892_CR19 publication-title: Proc AAAI Conf Artif Intell doi: 10.1609/aaai.v38i2.27929 – volume: 367 start-page: 953 year: 2016 ident: 16892_CR32 publication-title: Inf Sci doi: 10.1016/j.ins.2016.07.030 – volume: 11 start-page: 100 issue: 6 year: 2022 ident: 16892_CR7 publication-title: Computers doi: 10.3390/computers11060100 – ident: 16892_CR14 doi: 10.36948/ijfmr.2024.v06i06.33087 – ident: 16892_CR42 doi: 10.48550/arXiv.1907.03739 – volume: 2 start-page: 32 issue: 1 year: 2014 ident: 16892_CR22 publication-title: Adv Manuf doi: 10.1007/s40436-014-0059-0 – ident: 16892_CR46 doi: 10.48550/arXiv.2503.18283 – ident: 16892_CR3 doi: 10.1088/1757-899X/1258/1/012022 – ident: 16892_CR51 doi: 10.48550/arXiv.1312.6114 – ident: 16892_CR2 doi: 10.1051/metal/2025032 – ident: 16892_CR33 doi: 10.1007/978-3-030-86446-0_38 – volume: 162 year: 2021 ident: 16892_CR30 publication-title: Thin-Walled Structures doi: 10.1016/j.tws.2021.107540 – volume: 37 start-page: 1234 year: 2023 ident: 16892_CR44 publication-title: Proc AAAI Conf Artif Intell doi: 10.1609/aaai.v38i3.28018 – ident: 16892_CR15 doi: 10.48084/etasr.8619 – volume: 36 start-page: 106 year: 2017 ident: 16892_CR31 publication-title: Swarm Evol Comput doi: 10.1016/j.swevo.2017.04.006 – ident: 16892_CR10 doi: 10.17973/MMSJ.2024_11_2024096 – ident: 16892_CR8 doi: 10.1109/AIIPCC57291.2022.00086 – volume: 24 start-page: 4660 issue: 14 year: 2024 ident: 16892_CR40 publication-title: Sensors doi: 10.3390/s24144660 – ident: 16892_CR52 doi: 10.48550/arXiv.1610.07584 – ident: 16892_CR24 doi: 10.1088/2631-8695/ac3e13 – ident: 16892_CR25 doi: 10.1016/j.rcim.2019.101843 – volume: 133 start-page: 4331 issue: 9 year: 2024 ident: 16892_CR5 publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-024-13827-x – ident: 16892_CR35 doi: 10.1016/j.rcim.2023.102687 – volume: 41 start-page: 1324 year: 2024 ident: 16892_CR39 publication-title: Manuf Lett doi: 10.1016/j.mfglet.2024.09.159 – ident: 16892_CR21 doi: 10.1109/TPAMI.2022.3194555 – ident: 16892_CR27 doi: 10.5445/IR/1000129520 – volume: 21 start-page: 8480 issue: 24 year: 2021 ident: 16892_CR37 publication-title: Sensors doi: 10.3390/s21248480 – ident: 16892_CR49 – ident: 16892_CR20 doi: 10.48550/arXiv.1909.12037 – ident: 16892_CR1 doi: 10.1002/9781119932475 – ident: 16892_CR47 doi: 10.48550/arXiv.1512.03012 – ident: 16892_CR50 doi: 10.48550/arXiv.1706.02413 – ident: 16892_CR11 doi: 10.1109/TIM.2024.3480198 – volume: 133 start-page: 253 year: 2015 ident: 16892_CR23 publication-title: Reliab Eng Syst Safety doi: 10.1016/j.ress.2014.09.018 – ident: 16892_CR41 doi: 10.1109/ACCESS.2025.3525567 – ident: 16892_CR9 doi: 10.15282/ijame.18.2.2021.02.0658 – ident: 16892_CR34 doi: 10.1007/978-3-030-52071-7_20 – volume: 10 start-page: 779 issue: 9 year: 2022 ident: 16892_CR29 publication-title: Machines doi: 10.3390/machines10090779 – volume: 56 start-page: 84 year: 2020 ident: 16892_CR26 publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2020.05.008 – ident: 16892_CR45 doi: 10.48550/arXiv.2203.10314 – volume: 51 start-page: 1055 year: 2010 ident: 16892_CR6 publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-010-2684-5 – volume: 11 start-page: 43370 year: 2023 ident: 16892_CR18 publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3271748 – ident: 16892_CR36 doi: 10.1109/TIM.2025.3571125 – ident: 16892_CR48 doi: 10.1109/CVPR.2015.7298801 – volume: 23 start-page: 8541 issue: 20 year: 2023 ident: 16892_CR38 publication-title: Sensors doi: 10.3390/s23208541
SSID	ssj0016168 ssib034539549 ssib019759004 ssib029851711
Score	2.447583
Snippet	The automated inspection of complex freeform components, such as helical gears, is a persistent challenge in advanced manufacturing due to their intricate...
SourceID	proquest crossref springer
SourceType	Aggregation Database Index Database Publisher
StartPage	3695
SubjectTerms	Accuracy Algorithms Automation CAD CAE) and Design Computer aided design Computer-Aided Engineering (CAD Coordinate measuring machines Datasets Deep learning Defects Engineering Helical gears Industrial and Production Engineering Industrial applications Inspection Inspections Machine learning Manufacturing Measurement techniques Mechanical Engineering Media Management Neural networks Noise sensitivity Noise tolerance Optics Optimization Original Article Perturbation Product reliability
Title	Deep learning-based automated 3D inspection of helical gears using voxelized CAD models and 3D convolutional autoencoders
URI	https://link.springer.com/article/10.1007/s00170-025-16892-y https://www.proquest.com/docview/3276667270
Volume	141
WOSCitedRecordID	wos001610273800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAVX databaseName: SpringerLink Journals customDbUrl: eissn: 1433-3015 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016168 issn: 0268-3768 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1JT4QwFG7cDnpwN46Opgdv2oSWpe3RuMSDMcYtcyOltKOJASNoHH-9rwVm1OhBbwTaEvq-x_vgbQjtgVnQiQ4p0VpnJIp5RFQWhSQONAVzEAulvaTP-cWFGAzkZZsUVnXR7p1L0r-px8luvtQLce1XaSIkI6NpNAvmTjh1vLq-61BEJXedMMcoY9K1n5-gOIzisPFttb6GhPqEOfgYEU7dRJta8_M9v5qvCSf95kb11ul06X_PtYwWWzaKDxv4rKApU6yihU81CtfQ6NiYJ9w2lxgSZ_VyrF7qEqguHIXH-KFo0jXLApcW3xv3G_ARD2FGhV1Y_RC_lm9w9h2Gg-Cxb75TYVX42S7svYU_zHILu9KaLrx6Hd2entwcnZG2XwPRjLOaUKpjwTkocm6sZCYxIs-pkkAqrBaaBbmIFafK2CiTJrJ5DORU28xIC6SSs3ADzRRlYTYRVhkM10msFKDH2FAxZiMD9DKRRgWJ6qH9TgzpU1OWIx0XYPYbmsKGpn5D01EP9TtJpa2KVmnIeOLuyoMeOugkM7n8-2pbfxu-jeaZF64Lgemjmfr5xeygOf1aP1TPux66Hw-b5fc
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT9wwEB4VigQcaKEgtqXUh97A0tp52D5WBUTVZYUoIG6R49jLSihBZEEsv56xN9kFRA9wixI_FM_nzOfMC-AnqgWTmohRY0xO40TEVOdxRJOuYagOEqlNkHRP9Pvy4kIdN0Fhdevt3pokw5d6GuwWUr1QX36VpVJxOp6DjzFqLO_Id_LvvEURU8JXwpyijCtffn6G4ihOooltq7E1pCwEzOFhRPrtJpvQmtfnfK6-Zpz0hRk1aKeDT-97r8-w0rBR8msCn1X4YMs1WH6So_ALjPesvSZNcYkB9VqvIPp2VCHVxatojwzLSbhmVZLKkUvrfwNekQH2qIl3qx-Qu-oe7z5gcxQ8CcV3aqLL0Nu7vTfwx15-YJ9a07tXr8PZwf7p70Pa1Gughgs-ooyZRAqBG7mwTnGbWlkUTCskFc5Iw7uFTLRg2ro4VzZ2RYLk1LjcKoekUvBoA-bLqrSbQHSOzU2aaI3osS7SnLvYIr1MldXdVHdgpxVDdj1Jy5FNEzCHBc1wQbOwoNm4A1utpLJmi9ZZxEXqZxXdDuy2kpk9_v9oX9_W_AcsHp4e9bLen_7fb7DEg6C9O8wWzI9ubu13WDB3o2F9sx1g_AjfPOjb
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9wwEB5RQBU9FGhBbAutD9xai7XzsH1ELKsi0AoJqLhFjmMvSChZsQGx_fUdO8kuoPZQ9RYltqN4ZjSfM48PYB_dgklNxKgxJqdxImKq8ziiSd8wdAeJ1CZI-kyMRvL6Wp0_q-IP2e5dSLKpafBdmsr6YFK4g3nhW2j7Qj0VK0ul4nT2BlZiTxrkz-sXPzuNYkp4Vsy5xnHlqegXGh3FSdTEudq4Q8pC8RweTKQ3PdmW2fz5nS9d2QKfvgqpBk81XP__b9yA9y1KJYeNWm3Cki0_wLtnvQs_wmxg7YS0pBNj6r1hQfRDXSEExqtoQG7LpoyzKknlyI31vwfvyBhnTIlPtx-Tx-oJ7_7C4agQJJDyTIkuw2yfDt-aBc7yC_uWmz7teguuhseXRz9oy-NADRe8poyZRAqBBl5Yp7hNrSwKphWCDWek4f1CJlowbV2cKxu7IkHQalxulUOwKXi0DctlVdodIDrH4SZNtEatsi7SnLvYIuxMldX9VPfgWyeSbNK068jmjZnDhma4oVnY0GzWg91OallrutMs4iL1bxX9HnzvpLR4_PfVPv3b8K_w9nwwzM5ORqefYY0HOfssmV1Yru8f7B6smsf6dnr_JWj0bwyz8b8
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Deep+learning-based+automated+3D+inspection+of+helical+gears+using+voxelized+CAD+models+and+3D+convolutional+autoencoders&rft.jtitle=International+journal+of+advanced+manufacturing+technology&rft.au=Selloum%2C+Rabia&rft.au=Ameddah%2C+Hacene&rft.au=Brioua%2C+Mourad&rft.date=2025-12-01&rft.issn=0268-3768&rft.eissn=1433-3015&rft_id=info:doi/10.1007%2Fs00170-025-16892-y&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s00170_025_16892_y
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0268-3768&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0268-3768&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0268-3768&client=summon