Data-driven non-intrusive reduced order modelling of selective laser melting additive manufacturing process using proper orthogonal decomposition and convolutional autoencoder

This study proposes and compares two data-driven, non-intrusive reduced-order models (ROMs) for additive manufacturing (AM) processes: a combined proper orthogonal decomposition-artificial neural network (POD-ANN) and a convolutional autoencoder-multilayer perceptron (CAE-MLP). The POD-ANN model uti...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Advanced modeling and simulation in engineering sciences Ročník 12; číslo 1; s. 22 - 23
Hlavní autoři:	Chaudhry, Shubham, Abdedou, Azzedine, Soulaïmani, Azzeddine
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cham Springer International Publishing 01.12.2025 Springer Nature B.V SpringerOpen
Témata:	Accuracy Additive manufacturing Artificial neural networks Classical and Continuum Physics Complexity Computational Science and Engineering Convolutional autoencoder Costs Datasets Decomposition Deep learning Engineering Laser beam melting Lasers Machine learning Manufacturing Mechanical analysis Model reduction Multilayer perceptrons Neural networks Optimization Proper Orthogonal Decomposition Reduced order models Reduced-order model Regression models Simulation Statistical analysis Temperature Theoretical and Applied Mechanics Thermomechanical analysis Deep learning Proper orthogonal decomposition Additive manufacturing Convolutional autoencoder Reduced-order model
ISSN:	2213-7467, 2213-7467
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	This study proposes and compares two data-driven, non-intrusive reduced-order models (ROMs) for additive manufacturing (AM) processes: a combined proper orthogonal decomposition-artificial neural network (POD-ANN) and a convolutional autoencoder-multilayer perceptron (CAE-MLP). The POD-ANN model utilizes proper orthogonal decomposition to create a reduced-order model, which is then combined with an artificial neural network to establish a surrogate model linking the snapshot matrix to the input parameters. This approach effectively reduces the dimensionality of the high-fidelity snapshot matrix and constructs a regression framework for accurate predictions. Conversely, the CAE-MLP model employs a 1D convolutional autoencoder to reduce the spatial dimension of a high-fidelity snapshot matrix derived from numerical simulations. The compressed latent space is then projected onto the input variables using a multilayer perceptron (MLP) regression model. This method leverages deep learning techniques to handle the complexity of the data and improve prediction accuracy. The accuracy and efficiency of both models are evaluated through thermo-mechanical analysis of an AM-built part. The comparison of statistical moments from high-fidelity simulation results with ROM predictions reveals a strong correlation. Furthermore, the predictions are validated against experimental results at various locations. While both models demonstrate good agreement with experimental data, the CAE-MLP model outperforms the POD-ANN model in terms of prediction accuracy and performance. The findings highlight the potential of integrating reduced-order modeling techniques with machine learning algorithms to enhance the analysis of complex AM processes. The proposed models offer a robust framework for future research and applications in the field of additive manufacturing, providing high precision and efficiency.
AbstractList	This study proposes and compares two data-driven, non-intrusive reduced-order models (ROMs) for additive manufacturing (AM) processes: a combined proper orthogonal decomposition-artificial neural network (POD-ANN) and a convolutional autoencoder-multilayer perceptron (CAE-MLP). The POD-ANN model utilizes proper orthogonal decomposition to create a reduced-order model, which is then combined with an artificial neural network to establish a surrogate model linking the snapshot matrix to the input parameters. This approach effectively reduces the dimensionality of the high-fidelity snapshot matrix and constructs a regression framework for accurate predictions. Conversely, the CAE-MLP model employs a 1D convolutional autoencoder to reduce the spatial dimension of a high-fidelity snapshot matrix derived from numerical simulations. The compressed latent space is then projected onto the input variables using a multilayer perceptron (MLP) regression model. This method leverages deep learning techniques to handle the complexity of the data and improve prediction accuracy. The accuracy and efficiency of both models are evaluated through thermo-mechanical analysis of an AM-built part. The comparison of statistical moments from high-fidelity simulation results with ROM predictions reveals a strong correlation. Furthermore, the predictions are validated against experimental results at various locations. While both models demonstrate good agreement with experimental data, the CAE-MLP model outperforms the POD-ANN model in terms of prediction accuracy and performance. The findings highlight the potential of integrating reduced-order modeling techniques with machine learning algorithms to enhance the analysis of complex AM processes. The proposed models offer a robust framework for future research and applications in the field of additive manufacturing, providing high precision and efficiency. This study proposes and compares two data-driven, non-intrusive reduced-order models (ROMs) for additive manufacturing (AM) processes: a combined proper orthogonal decomposition-artificial neural network (POD-ANN) and a convolutional autoencoder-multilayer perceptron (CAE-MLP). The POD-ANN model utilizes proper orthogonal decomposition to create a reduced-order model, which is then combined with an artificial neural network to establish a surrogate model linking the snapshot matrix to the input parameters. This approach effectively reduces the dimensionality of the high-fidelity snapshot matrix and constructs a regression framework for accurate predictions. Conversely, the CAE-MLP model employs a 1D convolutional autoencoder to reduce the spatial dimension of a high-fidelity snapshot matrix derived from numerical simulations. The compressed latent space is then projected onto the input variables using a multilayer perceptron (MLP) regression model. This method leverages deep learning techniques to handle the complexity of the data and improve prediction accuracy. The accuracy and efficiency of both models are evaluated through thermo-mechanical analysis of an AM-built part. The comparison of statistical moments from high-fidelity simulation results with ROM predictions reveals a strong correlation. Furthermore, the predictions are validated against experimental results at various locations. While both models demonstrate good agreement with experimental data, the CAE-MLP model outperforms the POD-ANN model in terms of prediction accuracy and performance. The findings highlight the potential of integrating reduced-order modeling techniques with machine learning algorithms to enhance the analysis of complex AM processes. The proposed models offer a robust framework for future research and applications in the field of additive manufacturing, providing high precision and efficiency.This study proposes and compares two data-driven, non-intrusive reduced-order models (ROMs) for additive manufacturing (AM) processes: a combined proper orthogonal decomposition-artificial neural network (POD-ANN) and a convolutional autoencoder-multilayer perceptron (CAE-MLP). The POD-ANN model utilizes proper orthogonal decomposition to create a reduced-order model, which is then combined with an artificial neural network to establish a surrogate model linking the snapshot matrix to the input parameters. This approach effectively reduces the dimensionality of the high-fidelity snapshot matrix and constructs a regression framework for accurate predictions. Conversely, the CAE-MLP model employs a 1D convolutional autoencoder to reduce the spatial dimension of a high-fidelity snapshot matrix derived from numerical simulations. The compressed latent space is then projected onto the input variables using a multilayer perceptron (MLP) regression model. This method leverages deep learning techniques to handle the complexity of the data and improve prediction accuracy. The accuracy and efficiency of both models are evaluated through thermo-mechanical analysis of an AM-built part. The comparison of statistical moments from high-fidelity simulation results with ROM predictions reveals a strong correlation. Furthermore, the predictions are validated against experimental results at various locations. While both models demonstrate good agreement with experimental data, the CAE-MLP model outperforms the POD-ANN model in terms of prediction accuracy and performance. The findings highlight the potential of integrating reduced-order modeling techniques with machine learning algorithms to enhance the analysis of complex AM processes. The proposed models offer a robust framework for future research and applications in the field of additive manufacturing, providing high precision and efficiency. Abstract This study proposes and compares two data-driven, non-intrusive reduced-order models (ROMs) for additive manufacturing (AM) processes: a combined proper orthogonal decomposition-artificial neural network (POD-ANN) and a convolutional autoencoder-multilayer perceptron (CAE-MLP). The POD-ANN model utilizes proper orthogonal decomposition to create a reduced-order model, which is then combined with an artificial neural network to establish a surrogate model linking the snapshot matrix to the input parameters. This approach effectively reduces the dimensionality of the high-fidelity snapshot matrix and constructs a regression framework for accurate predictions. Conversely, the CAE-MLP model employs a 1D convolutional autoencoder to reduce the spatial dimension of a high-fidelity snapshot matrix derived from numerical simulations. The compressed latent space is then projected onto the input variables using a multilayer perceptron (MLP) regression model. This method leverages deep learning techniques to handle the complexity of the data and improve prediction accuracy. The accuracy and efficiency of both models are evaluated through thermo-mechanical analysis of an AM-built part. The comparison of statistical moments from high-fidelity simulation results with ROM predictions reveals a strong correlation. Furthermore, the predictions are validated against experimental results at various locations. While both models demonstrate good agreement with experimental data, the CAE-MLP model outperforms the POD-ANN model in terms of prediction accuracy and performance. The findings highlight the potential of integrating reduced-order modeling techniques with machine learning algorithms to enhance the analysis of complex AM processes. The proposed models offer a robust framework for future research and applications in the field of additive manufacturing, providing high precision and efficiency.
ArticleNumber	22
Author	Abdedou, Azzedine Chaudhry, Shubham Soulaïmani, Azzeddine
Author_xml	– sequence: 1 givenname: Shubham surname: Chaudhry fullname: Chaudhry, Shubham organization: Department of Mechanical Engineering, École de Technologie Supérieure – sequence: 2 givenname: Azzedine surname: Abdedou fullname: Abdedou, Azzedine organization: Department of Mechanical Engineering, École de Technologie Supérieure – sequence: 3 givenname: Azzeddine surname: Soulaïmani fullname: Soulaïmani, Azzeddine email: Azzeddine.soulaimani@etsmtl.ca organization: Department of Mechanical Engineering, École de Technologie Supérieure
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/40777044$$D View this record in MEDLINE/PubMed
BookMark	eNp9Ustu1TAQjVARLaU_wAJFYsMm4FfsZIVQeVWqxAbW1sR2bnPleIKdXKlfxS_i3JTSsmBlz8w5Z0Yz53lxEjC4onhJyVtKG_kuCcIZrwirK0I4qSv5pDhjjPJKCalOHvxPi4uU9oQQKrmgSj4rTgVRShEhzopfH2GGysbh4EKZW1RDmOOSclhGZxfjbInRuliOaJ33Q9iV2JfJeWfmFeQhrUXn57UE1g7H9Ahh6cHMS1zTU0TjUiqz7hZNmYNxvsEdBvCldQbHCVPmYigh2NJgOKBf1jjXYZnRBZMniC-Kpz345C7u3vPix-dP3y-_VtffvlxdfriuTM35XDEmuJTcmr7rZdNJpoCApETRnipqeC9bVnMjiekN7WsDpG2Ypa2QQDKo4-fF1aZrEfZ6isMI8VYjDPqYwLjTEOfBeKcpmK61hHVC1oKYrquhk41qFLXOKkOy1vtNa1q60Vnj8orBPxJ9XAnDjd7hQVPGWS14mxXe3ClE_Lm4NOtxSCbfA4LDJensBKVyT1Fn6Ot_oHtcYt7iESVV03BOM-rVw5HuZ_ljjAxgG8BETCm6_h5CiV4NqDcD6mxAfTSglpnEN1Ka1ru7-Lf3f1i_Ae_w4oI
Cites_doi	10.3390/app11178010 10.1016/j.ress.2019.106733 10.1016/j.cma.2018.10.047 10.1016/j.mtcomm.2021.102796 10.3390/app12052324 10.1016/j.engappai.2023.105908 10.1080/10407790590935920 10.1007/s40192-019-00149-0 10.1007/s11837-019-03792-2 10.1016/j.camwa.2021.01.015 10.1007/s11837-020-04155-y 10.21203/rs.3.rs-2443614/v1 10.1109/ICMLA.2019.00171 10.1002/cjce.23669 10.1063/5.0039986 10.1016/j.jtice.2021.04.062 10.48550/arXiv.2202.12653 10.1016/j.rcim.2019.01.004 10.55092/am20250001 10.1007/s00138-021-01226-1 10.1016/j.camwa.2021.10.006 10.48550/arXiv.2412.18786 10.1016/j.jprocont.2020.08.002 10.1016/j.mfglet.2019.02.001 10.1007/s10845-021-01879-9 10.1016/j.mfglet.2018.10.002 10.2514/6.2019-3333 10.1007/s00466-022-02257-9 10.1016/j.addma.2020.101538 10.1016/j.jcp.2020.109854 10.1002/nme.1205 10.7302/23070 10.1109/ACCESS.2018.2836917 10.1016/j.cma.2016.12.033 10.1080/14786440109462720 10.1109/WSC.2002.1166440 10.1007/s00170-008-1669-0
ContentType	Journal Article
Copyright	The Author(s) 2025 The Author(s) 2025. The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2025 2025
Copyright_xml	– notice: The Author(s) 2025 – notice: The Author(s) 2025. – notice: The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2025 2025
DBID	C6C AAYXX CITATION NPM 8FE 8FG ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO HCIFZ L6V M7S P5Z P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PTHSS 7X8 5PM DOA
DOI	10.1186/s40323-025-00305-6
DatabaseName	Springer Nature OA Free Journals CrossRef PubMed ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Technology collection ProQuest One Community College ProQuest Central SciTech Premium Collection ProQuest Engineering Collection Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Databases ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition Engineering collection MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef PubMed Publicly Available Content Database Advanced Technologies & Aerospace Collection Engineering Database Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest One Academic UKI Edition ProQuest Central Korea Materials Science & Engineering Collection ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) Engineering Collection MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic PubMed CrossRef Publicly Available Content Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	2213-7467
EndPage	23
ExternalDocumentID	oai_doaj_org_article_1acb9d02b46540cbb5ab687871ded7c0 PMC12325439 40777044 10_1186_s40323_025_00305_6
Genre	Journal Article
GrantInformation_xml	– fundername: Natural Sciences and Engineering Research Council of Canada grantid: RGPIN/2693-2021 funderid: https://doi.org/10.13039/501100000038
GroupedDBID	0R~ 5VS 8FE 8FG AAFWJ AAJSJ AAKKN AASML ABEEZ ABJCF ACACY ACGFS ACULB ADBBV ADMLS AFGXO AFKRA AFPKN AHBYD AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP ARAPS ARCSS ASPBG BCNDV BENPR BGLVJ C24 C6C CCPQU EBLON EBS GROUPED_DOAJ HCIFZ IAO ITC KQ8 L6V M7S M~E OK1 P62 PHGZM PHGZT PIMPY PQGLB PROAC PTHSS SOJ TUS AAYXX AFFHD CITATION AHSBF EJD H13 NPM ABUWG AZQEC DWQXO PKEHL PQEST PQQKQ PQUKI PUEGO 7X8 5PM
ID	FETCH-LOGICAL-c533t-2243663dcfbf68b627a0a61071f171c3f69253c60cfc1f5ca0982d1946a0071b3
IEDL.DBID	M7S
ISICitedReferencesCount	0
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001544693800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2213-7467
IngestDate	Fri Oct 03 12:51:54 EDT 2025 Tue Nov 04 02:03:37 EST 2025 Fri Sep 05 15:16:16 EDT 2025 Tue Sep 30 19:05:40 EDT 2025 Mon Aug 11 01:33:22 EDT 2025 Sat Nov 29 07:34:57 EST 2025 Wed Aug 06 16:36:44 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	1
Keywords	Deep learning Proper orthogonal decomposition Additive manufacturing Convolutional autoencoder Reduced-order model
Language	English
License	The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c533t-2243663dcfbf68b627a0a61071f171c3f69253c60cfc1f5ca0982d1946a0071b3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
OpenAccessLink	https://www.proquest.com/docview/3236788331?pq-origsite=%requestingapplication%
PMID	40777044
PQID	3236788331
PQPubID	2034555
PageCount	23
ParticipantIDs	doaj_primary_oai_doaj_org_article_1acb9d02b46540cbb5ab687871ded7c0 pubmedcentral_primary_oai_pubmedcentral_nih_gov_12325439 proquest_miscellaneous_3237768745 proquest_journals_3236788331 pubmed_primary_40777044 crossref_primary_10_1186_s40323_025_00305_6 springer_journals_10_1186_s40323_025_00305_6
PublicationCentury	2000
PublicationDate	2025-12-01
PublicationDateYYYYMMDD	2025-12-01
PublicationDate_xml	– month: 12 year: 2025 text: 2025-12-01 day: 01
PublicationDecade	2020
PublicationPlace	Cham
PublicationPlace_xml	– name: Cham – name: Netherlands – name: Heidelberg
PublicationTitle	Advanced modeling and simulation in engineering sciences
PublicationTitleAbbrev	Adv. Model. and Simul. in Eng. Sci
PublicationTitleAlternate	Adv Model Simul Eng Sci
PublicationYear	2025
Publisher	Springer International Publishing Springer Nature B.V SpringerOpen
Publisher_xml	– name: Springer International Publishing – name: Springer Nature B.V – name: SpringerOpen
References	305_CR3 305_CR40 305_CR2 305_CR41 305_CR5 A Chatterjee (305_CR12) 2000; 78 305_CR22 305_CR44 305_CR23 305_CR45 305_CR1 305_CR20 305_CR42 HMD Kabir (305_CR43) 2018; 6 305_CR21 305_CR26 305_CR27 305_CR24 305_CR25 305_CR28 305_CR29 R Siddalingappa (305_CR33) 2021; 12 X Zhao (305_CR18) 2021; 29 305_CR30 305_CR11 305_CR34 305_CR31 305_CR10 305_CR32 305_CR15 305_CR37 305_CR16 305_CR38 S Chaudhry (305_CR4) 2022; 12 305_CR13 305_CR35 305_CR14 305_CR36 305_CR19 305_CR17 305_CR39 305_CR7 305_CR6 305_CR9 305_CR8
References_xml	– ident: 305_CR3 doi: 10.3390/app11178010 – ident: 305_CR13 doi: 10.1016/j.ress.2019.106733 – ident: 305_CR37 – ident: 305_CR17 doi: 10.1016/j.cma.2018.10.047 – ident: 305_CR35 – volume: 29 start-page: 102796 year: 2021 ident: 305_CR18 publication-title: Mater Today Commun doi: 10.1016/j.mtcomm.2021.102796 – volume: 12 start-page: 2324 issue: 5 year: 2022 ident: 305_CR4 publication-title: Appl Sci doi: 10.3390/app12052324 – ident: 305_CR24 doi: 10.1016/j.engappai.2023.105908 – ident: 305_CR20 doi: 10.1080/10407790590935920 – ident: 305_CR36 doi: 10.1007/s40192-019-00149-0 – ident: 305_CR9 doi: 10.1007/s11837-019-03792-2 – ident: 305_CR16 doi: 10.1016/j.camwa.2021.01.015 – ident: 305_CR1 doi: 10.1007/s11837-020-04155-y – ident: 305_CR26 doi: 10.21203/rs.3.rs-2443614/v1 – ident: 305_CR29 doi: 10.1109/ICMLA.2019.00171 – ident: 305_CR28 doi: 10.1002/cjce.23669 – ident: 305_CR31 – volume: 12 start-page: 10 issue: 7 year: 2021 ident: 305_CR33 publication-title: Int J Adv Comput Sci Appl – ident: 305_CR39 doi: 10.1063/5.0039986 – ident: 305_CR30 doi: 10.1016/j.jtice.2021.04.062 – ident: 305_CR45 doi: 10.48550/arXiv.2202.12653 – ident: 305_CR8 doi: 10.1016/j.rcim.2019.01.004 – ident: 305_CR25 doi: 10.55092/am20250001 – ident: 305_CR34 doi: 10.1007/s00138-021-01226-1 – ident: 305_CR14 doi: 10.1016/j.camwa.2021.10.006 – ident: 305_CR19 doi: 10.48550/arXiv.2412.18786 – ident: 305_CR40 – ident: 305_CR27 doi: 10.1016/j.jprocont.2020.08.002 – ident: 305_CR5 doi: 10.1016/j.mfglet.2019.02.001 – ident: 305_CR42 – ident: 305_CR32 doi: 10.1007/s10845-021-01879-9 – ident: 305_CR6 doi: 10.1016/j.mfglet.2018.10.002 – ident: 305_CR38 doi: 10.2514/6.2019-3333 – ident: 305_CR23 doi: 10.1007/s00466-022-02257-9 – ident: 305_CR2 doi: 10.1016/j.addma.2020.101538 – volume: 78 start-page: 808 issue: 7 year: 2000 ident: 305_CR12 publication-title: Curr Sci – ident: 305_CR15 doi: 10.1016/j.jcp.2020.109854 – ident: 305_CR21 doi: 10.1002/nme.1205 – ident: 305_CR44 doi: 10.7302/23070 – volume: 6 start-page: 36218 year: 2018 ident: 305_CR43 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2836917 – ident: 305_CR11 doi: 10.1016/j.cma.2016.12.033 – ident: 305_CR22 – ident: 305_CR10 doi: 10.1080/14786440109462720 – ident: 305_CR41 doi: 10.1109/WSC.2002.1166440 – ident: 305_CR7 doi: 10.1007/s00170-008-1669-0
SSID	ssj0001634176
Score	2.3105502
Snippet	This study proposes and compares two data-driven, non-intrusive reduced-order models (ROMs) for additive manufacturing (AM) processes: a combined proper... Abstract This study proposes and compares two data-driven, non-intrusive reduced-order models (ROMs) for additive manufacturing (AM) processes: a combined...
SourceID	doaj pubmedcentral proquest pubmed crossref springer
SourceType	Open Website Open Access Repository Aggregation Database Index Database Publisher
StartPage	22
SubjectTerms	Accuracy Additive manufacturing Artificial neural networks Classical and Continuum Physics Complexity Computational Science and Engineering Convolutional autoencoder Costs Datasets Decomposition Deep learning Engineering Laser beam melting Lasers Machine learning Manufacturing Mechanical analysis Model reduction Multilayer perceptrons Neural networks Optimization Proper Orthogonal Decomposition Reduced order models Reduced-order model Regression models Simulation Statistical analysis Temperature Theoretical and Applied Mechanics Thermomechanical analysis
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3JjtQwEC2hEQc4IIY1MIOMxA2iiZN4yREGRpxGHECam-Ut0BKkW73Mb_GLVFXSTTeLuHBM7ERuVzl-1X71CuAFBjhtjIo4TzGUrbKyDHXwlCujumTrPmjPxSbM5aW9uuo-7JX6Ik7YKA88TtyZ9DF0qaoDCX_h-4LyQVt0M5lyMpGj9cp0e8EU_7ui8ets9DZLxuqzVVs1NR1ZqpJxc6kPdiIW7P8TyvydLPnLiSlvRBd34c6EIMXrceTHcCMP9-D2nq7gffj-1q99mZb0JRMY35ezgXIr8FIsSao1J8GSm4Lr4FBCupj3YsUlcagTImpqzF-JEy2IcsS3v_lhQ4kQnNkoFmOKgSDmPF8t8Bk6BZp_JnQvUia6-sQJE35Ighjuk6dju9-s56SiieN4AJ8u3n08f19OlRlKNGuzLnHfbxCqpNiHXtuga-Mrj0DMyF4aGZted7Vqoq5iH2Wvoq86WyfZtdoTpgnNQzjCX58fg8D9MSQddYOhGoKLYGOfZZeUqnOH0CUX8HJrJbcYBTgcBy5Wu9GmDm3q2KZOF_CGDLnrSeLZfANdyk0u5f7lUgWcbN3ATSt65RqSuqPKzLKA57tmXIt0wOKHPN9wH4Phm2lVAY9Gr9mNBANnY6q2LcAe-NPBUA9bhtkX1vsm1KsQOBbwaut6P8f197l48j_m4incqmnNMIHnBI7QW_Mp3IzX69lq-YwX3Q_OxDUn priority: 102 providerName: Directory of Open Access Journals – databaseName: SpringerOpen dbid: C24 link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lj9QwDLZg4QAH3o_CgoLEDSqatknTIyysOK04gLS3KK8uI0E7ms7wt_Yvru1pBwaWAxzbpFJa28nn-rMN8BIdnDoERZyn4PNaGZn70jvKlVFtNGXnteNmE83JiTk9bT9NSWHjzHafQ5K8U7NZG_1mrIuqpJijyhn45voqXFPStETkO5pyHPjPisadudFzhsylj-6dQlys_zKE-SdR8rdoKR9Cx7f_b_l34NYEOsXbrZbchSupvwc3fylFeB_O37u1y-OKNj_RD32-6CkdAy_Fiqq7pii4Sqfg1jmUwy6GTozcRYcmIQinwfSNaNSCWEp8-7vrN5Q7wcmQYrnNShBEtuerJT5DgaPhjBwCERMx3CcamXB9FESKn4wDx91mPVDhTVzHA_hy_OHz0cd8auaQoyZU6xyhQoXoJobOd9p4XTaucIjdGtnJRoaq022pqqCL0AXZqeCK1pRRtrV2BIN89RAO8O3TYxB4pPqog67Qu0M84k3okmyjUmVqEe2kDF7NwrXLbc0Oy76O0XYrBotisCwGqzN4R_LfzaR623xjWJ3ZyXytdMG3sSg9lZ9DrfbKeW1ws5MxxSYUGRzO2mOnTWC0FVXHo2bOMoMXu2E0X4rJuD4NG57ToMfX1CqDR1tl260Efe2mKeo6A7OnhntL3R_pF1-5RDgBZYVYM4PXszb-XNffv8WTf5v-FG6UpNDM7jmEA9TL9Ayuhx_rxbh6zlZ5Acl3N8s priority: 102 providerName: Springer Nature
Title	Data-driven non-intrusive reduced order modelling of selective laser melting additive manufacturing process using proper orthogonal decomposition and convolutional autoencoder
URI	https://link.springer.com/article/10.1186/s40323-025-00305-6 https://www.ncbi.nlm.nih.gov/pubmed/40777044 https://www.proquest.com/docview/3236788331 https://www.proquest.com/docview/3237768745 https://pubmed.ncbi.nlm.nih.gov/PMC12325439 https://doaj.org/article/1acb9d02b46540cbb5ab687871ded7c0
Volume	12
WOSCitedRecordID	wos001544693800001&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2213-7467 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001634176 issn: 2213-7467 databaseCode: DOA dateStart: 20140101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2213-7467 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001634176 issn: 2213-7467 databaseCode: M~E dateStart: 20140101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2213-7467 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001634176 issn: 2213-7467 databaseCode: P5Z dateStart: 20181201 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2213-7467 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001634176 issn: 2213-7467 databaseCode: M7S dateStart: 20181201 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2213-7467 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001634176 issn: 2213-7467 databaseCode: BENPR dateStart: 20181201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2213-7467 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001634176 issn: 2213-7467 databaseCode: PIMPY dateStart: 20181201 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerOpen customDbUrl: eissn: 2213-7467 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001634176 issn: 2213-7467 databaseCode: C24 dateStart: 20141201 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/eLvHCXMwpV1Jj9MwFH5iZjjAgX0gMFRG4gbRxEm85ISYYUZwoKpYpMIl8pahEiSlC3-Lv8h7btpStguXSs5zK7v5_Pw9-y0Aj9HAKZ0T5PPkbFoKzVObW0OxMqLyOm-sNLHYhBoO9XhcjfoDt3nvVrnWiVFR-87RGflxQanGqDIufzb9mlLVKLpd7Uto7MEBZUng0XXv7faMRaKOVnIdK6Pl8bzM8IdSquEa2XMqd_ajmLb_T1zzd5fJX-5N43Z0fv1_J3IDrvVElD1fIecmXArtLbj6U3rC2_D9hVmY1M9IIbK2a9NJSyEa2GQzyvgaPIuZO1ksp0Nx7axr2DxW1qFOSMxJGD6TazUjz6X4-ItplxRPEQMk2XQVqcDIAT-2pvgdukzqLshIYD6Q13vvWsZM6xk5yvcLBuVmuegoGSeO4w68Pz97d_oy7Qs8pIiOYpEifSiQ8XjX2EZqK3NlMoN8TvGGK-6KRla5KJzMXON4I5zJKp17XpXSEDWyxSHs4-zDPWC4zVovnSzQ4kOOYrVrAq-8EHmokAGFBJ6sX3M9XeXxqKP9o2W9AkWNoKgjKGqZwAkhYdOTcnDHB93sou6XdM2Ns5XPcksp6RDpVhgrNSpA7oNXLkvgaA2AulcM83r79hN4tBHjkqZ7GtOGbhn7KLQCVSkSuLuC3WYkaH8rlZVlAnoHkDtD3ZW0k08xbTiRZ4H8M4Gna-xux_X3_-L-v6fxAK7ktJyih88R7CMOw0O47L4tJvPZAPbUWA_g4ORsOHqDrdO8HMRjj0Fcqfg5Eh9RPnr1evThB2zrSXU
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Jb9NAFH4qKRJwYF8MBQYJTmDV64x9QAgoVaO2UQ5FKid3NpdIYIc4AfGn4C_y3thOCNutB472jK3n8fe2mbcAPEYHJ9E6pZgnrfwkzUJfRUpSrkyamywqFZeu2YQYjbLj43y8Ad_6XBgKq-xlohPUpta0R74dU6kx6owbvph-8qlrFJ2u9i00Wljs269f0GVrng938P8-iaLdN0ev9_yuq4CPJMVzH3VWjGrW6FKVPFM8EjKQaESIsAxFqOOS51Eaax7oUodlqmWQZ5FBX59L0scqxveeg82EwD6AzfHwcPxutavDUSsI3mfnZHy7SQIk3aeusc5e9_maBnSNAv5k3f4epPnLSa1TgLtX_reluwqXO1ObvWx54xps2Oo6XPqpAOMN-L4j59I3MxL5rKorf1JREgpeshnVtLWGudqkzDUMosx9Vpescb2DaBK6HjRoP1DwOKPYLHf7o6wWlDHiUkDZtM3FYJRi4K6m-Awdl9Wn5AYxYymuvwueY7IyjFIBOpGA43Ixr6ncKNJxE96eyYrdggF-vb0DDA0JZbjmMfq0aIWpTJc2zE2aRjZHG8968LSHVTFtK5UUzsPLeNGCsEAQFg6EBffgFSFvOZOqjLsb9ey06IRWEUqtchNEioruIS-rVCqeoYgPjTVCBx5s9YArOtHXFCu0efBoOYxCi06iZGXrhZsj0M8VSerB7RbmS0qSQAgRJIkH2RoDrJG6PlJN3rvC6OQepGhhe_Cs55UVXX9fi7v__oyHcGHv6PCgOBiO9u_BxYhY2cUzbcEAMWnvw3n9eT5pZg86acDg5Ky56AeAnp6j
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB5BQQgOvB-BAkbiBlHzsuMcoWUFAq16AKk3y892JUhWu1n-Fn-RGSdZulAOiGNiR3Lib-zPmZlvAF7iAaeyllPMkzVpxWWemsJoypXhjZNFMELHYhP1fC5PTprjc1n8Mdp9ckkOOQ2k0tT2B0sXBhOX4mBdZWVB_keeRhKcistwhTxShPHDMd8h_mURuErXYsqWufDRnR0pCvdfxDb_DJr8zXMaN6TZrf9_ldtwcySj7M2Anjtwybd34cY5icJ78ONI9zp1K1oUWdu16aKlNA28ZCtSffWORfVOFkvqUG476wJbx-o61AnJOTX6rxRezSh6Kd7-ptsN5VTEJEm2HLIVGAXhx6slPkMOpe6UDgrMeYp8H8PLmG4do2D50WiwXW_6jgQ5cRz34cvs3efD9-lY5CFFhJR9ihSiRNbjbDBBSCOKWmcaOV2dh7zObRlEU_DSiswGmwduddbIwuVNJTTRI1M-gD18e_8IGG61xgkrSjz1IU8x0gafN47zwjfIgnwCr6aJVstBy0PFM5AUapgGhdOg4jQokcBbwsK2J-lwxxvd6lSNZq1ybU3jssKQLB2i3XBthMRFMHfe1TZLYH9CkhoXh7UqSTWPijznCbzYNqNZk69Gt77bxD41ngTriifwcADediQI9LrOqioBuQPJnaHutrSLsygdTgSaIwdN4PWEzF_j-vu3ePxv3Z_DteOjmfr0Yf7xCVwvCNsxAGgf9hCi_ilctd_7xXr1LBrrT4N2Q5Q
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=Data-driven+non-intrusive+reduced+order+modelling+of+selective+laser+melting+additive+manufacturing+process+using+proper+orthogonal+decomposition+and+convolutional+autoencoder&rft.jtitle=Advanced+modeling+and+simulation+in+engineering+sciences&rft.au=Chaudhry%2C+Shubham&rft.au=Abdedou%2C+Azzedine&rft.au=Soula%C3%AFmani%2C+Azzeddine&rft.date=2025-12-01&rft.issn=2213-7467&rft.eissn=2213-7467&rft.volume=12&rft.issue=1&rft.spage=22&rft_id=info:doi/10.1186%2Fs40323-025-00305-6&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2213-7467&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2213-7467&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2213-7467&client=summon