Machine learning based prediction of carbon concentration in carburized steel

The carbon concentration and hardness of a part’s surface can be enhanced through the carburizing-quenching process. The mechanical properties are affected by the carbon concentration distribution in the surface layer, which directly determines wear resistance and fatigue life. Carbon concentration...

Full description

Saved in:

Bibliographic Details
Published in:	Scientific reports Vol. 15; no. 1; pp. 33678 - 19
Main Authors:	Zhang, Yingtao, Tang, Zirong, Yin, Qing, Wang, Yanjie, Nie, Zhenguo
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 29.09.2025 Nature Publishing Group Nature Portfolio
Subjects:	20Cr2Ni4A 639/166 639/301 Accuracy Algorithms Carbon Carburizing Datasets Finite element method Hardness Heat treating Heat treatment Heat treatments Heuristic Humanities and Social Sciences Learning algorithms Machine learning Mathematical models Mean shift clustering Mechanical properties multidisciplinary Neural networks Numerical calculation Optimization Predictions Science Science (multidisciplinary) Simulation Slime molds Slime mould algorithm Titanium alloys 20Cr2Ni4A Mean shift clustering Carburizing Numerical calculation Slime mould algorithm
ISSN:	2045-2322, 2045-2322
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	The carbon concentration and hardness of a part’s surface can be enhanced through the carburizing-quenching process. The mechanical properties are affected by the carbon concentration distribution in the surface layer, which directly determines wear resistance and fatigue life. Carbon concentration distribution can be calculated using the FEA (Finite Element Analysis) method with an error no greater than 10% for the heat treatment engineer. However, predicting the results using machine learning method is more efficient and easier. In this paper, the carburizing numerical model is established for a 20Cr2Ni4A cylindrical part. The microstructures and hardness gradient were tested to verify the accuracy of the simulation model for the furnace samples after the carburizing process. By establishing an accurate numerical model of the carburizing process, multiple sets of process parameter combinations were designed for three different shapes: square, circle, and trapezoid. Large-scale calculations were performed, resulting in a total of 530,226 sets of samples obtained. Finally, a parameter adaptive BPNN (Backpropagation Neural Network) method based on mean shift clustering and slime mould algorithm is proposed for rapid prediction of carbon concentration distribution on the surface of parts after carburizing. By comparing the prediction accuracy and training time with other methods, the feasibility and effectiveness of the method are verified.
AbstractList	The carbon concentration and hardness of a part’s surface can be enhanced through the carburizing-quenching process. The mechanical properties are affected by the carbon concentration distribution in the surface layer, which directly determines wear resistance and fatigue life. Carbon concentration distribution can be calculated using the FEA (Finite Element Analysis) method with an error no greater than 10% for the heat treatment engineer. However, predicting the results using machine learning method is more efficient and easier. In this paper, the carburizing numerical model is established for a 20Cr2Ni4A cylindrical part. The microstructures and hardness gradient were tested to verify the accuracy of the simulation model for the furnace samples after the carburizing process. By establishing an accurate numerical model of the carburizing process, multiple sets of process parameter combinations were designed for three different shapes: square, circle, and trapezoid. Large-scale calculations were performed, resulting in a total of 530,226 sets of samples obtained. Finally, a parameter adaptive BPNN (Backpropagation Neural Network) method based on mean shift clustering and slime mould algorithm is proposed for rapid prediction of carbon concentration distribution on the surface of parts after carburizing. By comparing the prediction accuracy and training time with other methods, the feasibility and effectiveness of the method are verified. Abstract The carbon concentration and hardness of a part’s surface can be enhanced through the carburizing-quenching process. The mechanical properties are affected by the carbon concentration distribution in the surface layer, which directly determines wear resistance and fatigue life. Carbon concentration distribution can be calculated using the FEA (Finite Element Analysis) method with an error no greater than 10% for the heat treatment engineer. However, predicting the results using machine learning method is more efficient and easier. In this paper, the carburizing numerical model is established for a 20Cr2Ni4A cylindrical part. The microstructures and hardness gradient were tested to verify the accuracy of the simulation model for the furnace samples after the carburizing process. By establishing an accurate numerical model of the carburizing process, multiple sets of process parameter combinations were designed for three different shapes: square, circle, and trapezoid. Large-scale calculations were performed, resulting in a total of 530,226 sets of samples obtained. Finally, a parameter adaptive BPNN (Backpropagation Neural Network) method based on mean shift clustering and slime mould algorithm is proposed for rapid prediction of carbon concentration distribution on the surface of parts after carburizing. By comparing the prediction accuracy and training time with other methods, the feasibility and effectiveness of the method are verified. The carbon concentration and hardness of a part's surface can be enhanced through the carburizing-quenching process. The mechanical properties are affected by the carbon concentration distribution in the surface layer, which directly determines wear resistance and fatigue life. Carbon concentration distribution can be calculated using the FEA (Finite Element Analysis) method with an error no greater than 10% for the heat treatment engineer. However, predicting the results using machine learning method is more efficient and easier. In this paper, the carburizing numerical model is established for a 20Cr2Ni4A cylindrical part. The microstructures and hardness gradient were tested to verify the accuracy of the simulation model for the furnace samples after the carburizing process. By establishing an accurate numerical model of the carburizing process, multiple sets of process parameter combinations were designed for three different shapes: square, circle, and trapezoid. Large-scale calculations were performed, resulting in a total of 530,226 sets of samples obtained. Finally, a parameter adaptive BPNN (Backpropagation Neural Network) method based on mean shift clustering and slime mould algorithm is proposed for rapid prediction of carbon concentration distribution on the surface of parts after carburizing. By comparing the prediction accuracy and training time with other methods, the feasibility and effectiveness of the method are verified.The carbon concentration and hardness of a part's surface can be enhanced through the carburizing-quenching process. The mechanical properties are affected by the carbon concentration distribution in the surface layer, which directly determines wear resistance and fatigue life. Carbon concentration distribution can be calculated using the FEA (Finite Element Analysis) method with an error no greater than 10% for the heat treatment engineer. However, predicting the results using machine learning method is more efficient and easier. In this paper, the carburizing numerical model is established for a 20Cr2Ni4A cylindrical part. The microstructures and hardness gradient were tested to verify the accuracy of the simulation model for the furnace samples after the carburizing process. By establishing an accurate numerical model of the carburizing process, multiple sets of process parameter combinations were designed for three different shapes: square, circle, and trapezoid. Large-scale calculations were performed, resulting in a total of 530,226 sets of samples obtained. Finally, a parameter adaptive BPNN (Backpropagation Neural Network) method based on mean shift clustering and slime mould algorithm is proposed for rapid prediction of carbon concentration distribution on the surface of parts after carburizing. By comparing the prediction accuracy and training time with other methods, the feasibility and effectiveness of the method are verified.
ArticleNumber	33678
Author	Yin, Qing Zhang, Yingtao Tang, Zirong Wang, Yanjie Nie, Zhenguo
Author_xml	– sequence: 1 givenname: Yingtao surname: Zhang fullname: Zhang, Yingtao organization: College of Mechanical & Electrical Engineering, Hohai University – sequence: 2 givenname: Zirong surname: Tang fullname: Tang, Zirong email: tangzirong@hhu.edu.cn organization: College of Mechanical & Electrical Engineering, Hohai University – sequence: 3 givenname: Qing surname: Yin fullname: Yin, Qing organization: College of Mechanical & Electrical Engineering, Hohai University – sequence: 4 givenname: Yanjie surname: Wang fullname: Wang, Yanjie organization: College of Mechanical & Electrical Engineering, Hohai University – sequence: 5 givenname: Zhenguo surname: Nie fullname: Nie, Zhenguo email: zhenguonie@tsinghua.edu.cn organization: Department of Mechanical Engineering, Tsinghua University
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/41022993$$D View this record in MEDLINE/PubMed
BookMark	eNp9kU1PJCEQhonRqDv6BzxsOvHipZWPZhqOxqyuicaLngnQxcikB0boPri_frFb3Y0H4UCl8rxVRb0_0G6IARA6IficYCYuckO4FDWmvCaCM1KLHXRIccNryijd_S8-QMc5r3E5nMqGyH100BBMqZTsEN3fa_vsA1Q96BR8WFVGZ-iqbYLO28HHUEVXWZ1MiWwMFsKQ9JT3YcqPyf8pgjwA9Edoz-k-w_H7u0BP178er37Xdw83t1eXd7VtmBhqA7Qh3VIASIFbI81ScGugs0stGZPMEGco465cXqC2kVIwwy0WmhsmHFug27luF_VabZPf6PSqovZqSsS0UjoN3vagZOkoyq64w6RpNdHOYilky1vrHDG41Dqba21TfBkhD2rjs4W-1wHimBWjnLdStpQW9PQLuo5jCuWnE7XEAlNSqJ_v1Gg20H2O97H0AtAZsCnmnMB9IgSrN3PVbK4q5qrJXCWKiM2iXOCwgvSv9zeqv-XupGc
Cites_doi	10.1016/j.jnca.2020.102854 10.1016/j.future.2020.03.055 10.1007/s11665-023-08510-3 10.1016/j.petrol.2019.06.014 10.1109/ULTSYM.2019.8926078 10.1007/s11665-021-06035-1 10.2320/matertrans.M2016361 10.1007/s11665-017-2729-0 10.1115/PVP2018-84226 10.1007/s11665-009-9476-9 10.1108/IJSI-06-2023-0048 10.1115/1.4050560 10.1016/j.triboint.2022.107576 10.1007/s00521-015-1920-1 10.3390/f14010051 10.1016/j.cma.2022.115499 10.1109/TNNLS.2016.2634548 10.1111/ffe.13541 10.3390/coatings11081003 10.1016/j.cma.2021.114218 10.1098/rsta.2022.0406 10.1142/S0217984917400802 10.1016/j.ijfatigue.2022.106938 10.1109/ACCESS.2019.2948475 10.1098/rsta.2022.0386 10.1108/ILT-10-2023-0328 10.3390/ijerph20021043 10.1016/j.ijfatigue.2019.105432 10.1007/s11665-021-06236-8
ContentType	Journal Article
Copyright	The Author(s) 2025 2025. The Author(s). The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: The Author(s) 2025 – notice: 2025. The Author(s). – notice: The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	C6C AAYXX CITATION NPM 3V. 7X7 7XB 88A 88E 88I 8FE 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 DOA
DOI	10.1038/s41598-025-18531-8
DatabaseName	Springer Nature OA Free Journals CrossRef PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Journals ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One ProQuest Central Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Biological Sciences ProQuest Health & Medical Collection Medical Database Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef PubMed Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Biology Journals (Alumni Edition) ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	Publicly Available Content Database PubMed MEDLINE - Academic
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	Biology
EISSN	2045-2322
EndPage	19
ExternalDocumentID	oai_doaj_org_article_943884155f0147a1afc0989757cff1b0 41022993 10_1038_s41598_025_18531_8
Genre	Journal Article
GrantInformation_xml	– fundername: National Natural Science Foundation of China grantid: 52475185 funderid: https://doi.org/10.13039/501100001809 – fundername: National Natural Science Foundation of China grantid: 52475185
GroupedDBID	0R~ 4.4 53G 5VS 7X7 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AAKDD AASML ABDBF ABUWG ACGFS ACUHS ADBBV ADRAZ AENEX AEUYN AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK DWQXO EBD EBLON EBS ESX FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE KQ8 LK8 M1P M2P M7P M~E NAO OK1 PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RNT RNTTT RPM SNYQT UKHRP AAYXX AFFHD CITATION NPM 3V. 7XB 88A 8FK K9. M48 PKEHL PQEST PQUKI PRINS Q9U 7X8
ID	FETCH-LOGICAL-c438t-be241d68ee9807b9b685cbedc6a93393b1fb235f5f558ee749983b5c08a5b38f3
IEDL.DBID	DOA
ISICitedReferencesCount	0
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001586174900012&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2045-2322
IngestDate	Mon Oct 13 19:21:21 EDT 2025 Tue Sep 30 19:06:47 EDT 2025 Tue Oct 07 09:16:55 EDT 2025 Sat Oct 04 02:10:23 EDT 2025 Sat Nov 29 07:25:40 EST 2025 Tue Sep 30 01:10:30 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	1
Keywords	20Cr2Ni4A Mean shift clustering Carburizing Numerical calculation Slime mould algorithm
Language	English
License	2025. The Author(s).
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c438t-be241d68ee9807b9b685cbedc6a93393b1fb235f5f558ee749983b5c08a5b38f3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
OpenAccessLink	https://doaj.org/article/943884155f0147a1afc0989757cff1b0
PMID	41022993
PQID	3255608021
PQPubID	2041939
PageCount	19
ParticipantIDs	doaj_primary_oai_doaj_org_article_943884155f0147a1afc0989757cff1b0 proquest_miscellaneous_3255799722 proquest_journals_3255608021 pubmed_primary_41022993 crossref_primary_10_1038_s41598_025_18531_8 springer_journals_10_1038_s41598_025_18531_8
PublicationCentury	2000
PublicationDate	2025-09-29
PublicationDateYYYYMMDD	2025-09-29
PublicationDate_xml	– month: 09 year: 2025 text: 2025-09-29 day: 29
PublicationDecade	2020
PublicationPlace	London
PublicationPlace_xml	– name: London – name: England
PublicationTitle	Scientific reports
PublicationTitleAbbrev	Sci Rep
PublicationTitleAlternate	Sci Rep
PublicationYear	2025
Publisher	Nature Publishing Group UK Nature Publishing Group Nature Portfolio
Publisher_xml	– name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio
References	E Emary (18531_CR27) 2018; 29 LW Xu (18531_CR24) 2019; 7 BT Li (18531_CR21) 2020; 173 W Wang (18531_CR2) 2021; 44 CQ Luo (18531_CR30) 2022; 388 SP Zhu (18531_CR15) 2023; 14 IJ Hong (18531_CR1) 2020; 143 YJ Wang (18531_CR31) 2021; 30 18531_CR12 JY Guo (18531_CR17) 2021; 11 JP Zhang (18531_CR18) 2024; 76 18531_CR9 R Xue (18531_CR19) 2024; 24 CQ Luo (18531_CR16) 2022; 400 N Zhang (18531_CR4) 2022; 161 HK Kim (18531_CR6) 2017; 58 JW Rui (18531_CR28) 2019; 180 LM Shen (18531_CR10) 2010; 33 HL Deng (18531_CR7) 2022; 31 SP Zhu (18531_CR14) 2023; 381 CX Li (18531_CR23) 2021; 68 S Mirjalili (18531_CR26) 2016; 27 18531_CR29 L Shi (18531_CR8) 2022; 171 18531_CR22 YT Zhang (18531_CR5) 2017; 26 LY Wang (18531_CR13) 2023; 381 A Sugianto (18531_CR32) 2010; 19 L Bonaiti (18531_CR3) 2021; 143 J Chen (18531_CR11) 2023; 20 SM Li (18531_CR25) 2020; 111 BH Zheng (18531_CR20) 2017; 31
References_xml	– volume: 68 start-page: 1987 year: 2021 ident: 18531_CR23 publication-title: IEEE Trans. Circuits Syst. II Exp. Briefs – volume: 173 start-page: 102854 year: 2020 ident: 18531_CR21 publication-title: J Netw Comput Appl doi: 10.1016/j.jnca.2020.102854 – volume: 111 start-page: 300 year: 2020 ident: 18531_CR25 publication-title: Future Gener Comput Syst Int J eScience doi: 10.1016/j.future.2020.03.055 – ident: 18531_CR12 doi: 10.1007/s11665-023-08510-3 – volume: 180 start-page: 699 year: 2019 ident: 18531_CR28 publication-title: J Pet Sci Eng doi: 10.1016/j.petrol.2019.06.014 – ident: 18531_CR22 doi: 10.1109/ULTSYM.2019.8926078 – volume: 30 start-page: 8040 year: 2021 ident: 18531_CR31 publication-title: J Mater Eng Perform doi: 10.1007/s11665-021-06035-1 – volume: 58 start-page: 592 year: 2017 ident: 18531_CR6 publication-title: Mater Trans doi: 10.2320/matertrans.M2016361 – volume: 26 start-page: 3043 year: 2017 ident: 18531_CR5 publication-title: J Mater Eng Perform doi: 10.1007/s11665-017-2729-0 – ident: 18531_CR9 doi: 10.1115/PVP2018-84226 – volume: 19 start-page: 194 year: 2010 ident: 18531_CR32 publication-title: J Mater Eng Perform doi: 10.1007/s11665-009-9476-9 – volume: 14 start-page: 1000 year: 2023 ident: 18531_CR15 publication-title: Int J Struct Integr doi: 10.1108/IJSI-06-2023-0048 – volume: 143 start-page: 103402 year: 2021 ident: 18531_CR3 publication-title: J Mech Des doi: 10.1115/1.4050560 – volume: 171 start-page: 107576 year: 2022 ident: 18531_CR8 publication-title: Tribol Int doi: 10.1016/j.triboint.2022.107576 – volume: 27 start-page: 1053 year: 2016 ident: 18531_CR26 publication-title: Neural Comput Appl doi: 10.1007/s00521-015-1920-1 – ident: 18531_CR29 doi: 10.3390/f14010051 – volume: 24 start-page: 1253 year: 2024 ident: 18531_CR19 publication-title: J. Comput. Methods Sci. Eng. – volume: 400 start-page: 115499 year: 2022 ident: 18531_CR16 publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2022.115499 – volume: 33 start-page: 142 year: 2010 ident: 18531_CR10 publication-title: Rev Adv Mater – volume: 29 start-page: 681 year: 2018 ident: 18531_CR27 publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2016.2634548 – volume: 44 start-page: 2736 year: 2021 ident: 18531_CR2 publication-title: Eng Mater Struct doi: 10.1111/ffe.13541 – volume: 11 start-page: 1003 year: 2021 ident: 18531_CR17 publication-title: Coat doi: 10.3390/coatings11081003 – volume: 388 start-page: 114218 year: 2022 ident: 18531_CR30 publication-title: Comput Methods Appl Mech Engrg doi: 10.1016/j.cma.2021.114218 – volume: 381 start-page: 20220406 year: 2023 ident: 18531_CR14 publication-title: Phil Trans R Soc doi: 10.1098/rsta.2022.0406 – volume: 31 start-page: 19 year: 2017 ident: 18531_CR20 publication-title: Mod. Phys. Lett. B doi: 10.1142/S0217984917400802 – volume: 161 start-page: 106938 year: 2022 ident: 18531_CR4 publication-title: Int J Fatigue doi: 10.1016/j.ijfatigue.2022.106938 – volume: 7 start-page: 152690 year: 2019 ident: 18531_CR24 publication-title: IEEE Access. doi: 10.1109/ACCESS.2019.2948475 – volume: 381 start-page: 20220386 year: 2023 ident: 18531_CR13 publication-title: Philos Trans R Soc A doi: 10.1098/rsta.2022.0386 – volume: 76 start-page: 345 year: 2024 ident: 18531_CR18 publication-title: Ind. Lubr Tribol doi: 10.1108/ILT-10-2023-0328 – volume: 20 start-page: 1043 year: 2023 ident: 18531_CR11 publication-title: Int J Environ Res Public Health doi: 10.3390/ijerph20021043 – volume: 143 start-page: 105432 year: 2020 ident: 18531_CR1 publication-title: Int J Fatigue doi: 10.1016/j.ijfatigue.2019.105432 – volume: 31 start-page: 882 year: 2022 ident: 18531_CR7 publication-title: J Mater Eng Perform doi: 10.1007/s11665-021-06236-8
SSID	ssj0000529419
Score	2.460403
Snippet	The carbon concentration and hardness of a part’s surface can be enhanced through the carburizing-quenching process. The mechanical properties are affected by... The carbon concentration and hardness of a part's surface can be enhanced through the carburizing-quenching process. The mechanical properties are affected by... Abstract The carbon concentration and hardness of a part’s surface can be enhanced through the carburizing-quenching process. The mechanical properties are...
SourceID	doaj proquest pubmed crossref springer
SourceType	Open Website Aggregation Database Index Database Publisher
StartPage	33678
SubjectTerms	20Cr2Ni4A 639/166 639/301 Accuracy Algorithms Carbon Carburizing Datasets Finite element method Hardness Heat treating Heat treatment Heat treatments Heuristic Humanities and Social Sciences Learning algorithms Machine learning Mathematical models Mean shift clustering Mechanical properties multidisciplinary Neural networks Numerical calculation Optimization Predictions Science Science (multidisciplinary) Simulation Slime molds Slime mould algorithm Titanium alloys
SummonAdditionalLinks	– databaseName: Science Database dbid: M2P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lj9QwDLZgAYkL70dhQUXiBtG2TdMmJwSIFQdmtQeQ9hbluRpp1Q6dGST49dhpZlaIxwX1UqVp5MROPieObYCXMaigWi-ZEb7DDUrsmZJeMVQVApeIWK1JjsKf-pMTeXamTvOB2zpfq9ytiWmh9qOjM_IjTrGyyDG0frP6yihrFFlXcwqNq3ANNZuarnQtmtP9GQtZsdpaZV-ZisujNeIV-ZQ1ghFQ1Uz-gkcpbP-fdM3f7KQJfo5v_y_hd-BWVjzLt7Ok3IUrYbgHN-ZUlN_vw2KRblWGMqeROC8J33y5msiSQ9wrx1g6M1l8c-TrOOSAu-VySOXbafkDf0CpCRcP4Mvxh8_vP7KcaoG5lssNswGR3HcyBCWr3irbSeFs8K4zinPFbR1tw0XER2ClHvdJklvhKmmE5TLyh3AwjEN4DCWObxSGO-Ujth0q1YTadj7E6LxBOCzg1W7A9WqOqKGTJZxLPbNHI3t0Yo-WBbwjnuxrUjTsVDBO5zpPLq2wD5I0o4gbvt7UJrpKSdWL3sVY26qAwx1rdJ6ia33JlwJe7D_j5CKLiRnCuJ3r9ORajFQ_miVhT0lLe2XU7gp4vRONy8b_3qEn_6blKdxsSDrJ7qUO4WAzbcMzuO6-bZbr6XkS75_phgBi priority: 102 providerName: ProQuest
Title	Machine learning based prediction of carbon concentration in carburized steel
URI	https://link.springer.com/article/10.1038/s41598-025-18531-8 https://www.ncbi.nlm.nih.gov/pubmed/41022993 https://www.proquest.com/docview/3255608021 https://www.proquest.com/docview/3255799722 https://doaj.org/article/943884155f0147a1afc0989757cff1b0
Volume	15
WOSCitedRecordID	wos001586174900012&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: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: DOA dateStart: 20110101 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: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M7P dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: 7X7 dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M2P dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3di9QwEB_0TsEXOb_rnUsF37Rc2zRN8nh33KHgLkUU1qeQz2NBukd39-D8650k3fVExRcpDCVNQ_KbhJlhMjMAb7wTTjSWF4raFg0UzwrBrShQVXCEo8RqVAwU_shmMz6fi-5Wqa9wJyylB07AHYuGcB6knkdlnqlKeVMKLhhlxvtKR2u9ZOKWMZWyeteiqcQYJVMSfrzCMUI0WU2LIKKqgv8iiWLC_j9pmb95SKPguTiAh6PGmJ-kmT6CO65_DPdTDcmbJzCdxuuQLh_rP1zmQTDZ_GoILpgAe770uVGDxjcTghT7MVNuvuhj-2ZYfMcfkN3u21P4cnH--ex9MdZIKAwCsy60QxFsW-6c4CXTQrecGu2saZUgRBBdeV0T6vGh2ImhgcOJpqbkimrCPXkGe_2ydy8gR3g8VcQI63FsV4raVbq1zntjFcqxDN5u8ZJXKRWGjC5swmVCVyK6MqIreQanAdJdz5DGOjYgc-XIXPkv5mZwtGWIHM_WSpKQNS2ECFcZvN59xlMRXB2qd8tN6sNCTDDO-nli5G4mTTByUS3L4N2Wsz8H__uCXv6PBR3CgzpsweDWEkewtx427hXcM9frxWqYwF02Z5HyCeyfns-6T5O4r5FO6y5QhnS_-zDtvv4Am-T4cg
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VAoILb0qgQJDgBFGTONnYB4R4Va26u-qhSL0ZP6uVULLN7oLKj-I3MuMkWyEetx5QLlHiWHb8zcvjmQF47p1worA8UaUdoYHiq0RwKxJUFRzjKLEKFQKFx9V0yo-PxeEG_BhiYehY5cATA6O2jaE98h1GubIoMDR7Mz9NqGoUeVeHEhodLA7c2Tc02Rav9z_g-r7I892PR-_3kr6qQGIKxpeJdii07Ig7J3haaaFHvDTaWTNSaNwLpjOvc1Z6vEpsVKFJwJkuTcpVqRn3DPu9BJcLyixGRwXzw_WeDnnNikz0sTkp4zsLlI8Uw5aXCQnGLOG_yL9QJuBPuu1vftkg7nZv_m8_6hbc6BXr-G1HCbdhw9V34GpXavPsLkwm4dSoi_syGScxyW8bz1vyVBE648bHRrUa7wzFctZ9QuF4Vofnq3b2HT9AqnBf7sGnC5nLfdism9o9gBjX05eKGWE99u1SkbtMj6zz3liF4j6Cl8MCy3mXMUQGTz_jsoODRDjIAAfJI3hHGFi3pGzf4UHTnsieeUiBc-Ck-Xk0aCuVKW9SwUVVVsb7TKcRbA9QkD0LWshzHETwbP0amQd5hFTtmlXXpqLQaRz1Voe89UgK2gtA7TWCVwMUzzv_-4Qe_nssT-Ha3tFkLMf704NHcD0nyiAfn9iGzWW7co_hivm6nC3aJ4G0Yvh80RD9CVQwXgQ
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Jj9MwFH4aOoC4sC-BAYIEJ4iaxFnsA0LAUFHNtOoBpOHk8TqqhNKStqDhp_HreC9LR4jlNgeUS5Q4lh1_fovfBvDUO-FEZnmkclugguLLSHArIhQVHOPIsTLVBAofltMpPzoSsx340cfCkFtlTxMbQm0Xhs7Ih4xyZVFgaDL0nVvEbH_0avklogpSZGnty2m0EDlwp99QfVu9HO_jWj9L09G7D2_fR12FgchkjK8j7ZCB2YI7J3hcaqELnhvtrCkUKvqC6cTrlOUerxwblagecKZzE3OVa8Y9w34vwC6K5Fk6gN3ZeDL7tD3hIRtaloguUidmfLhCbkkRbWkeEZtMIv4LN2yKBvxJ0v3NStswv9G1__m3XYerncgdvm73yA3YcdVNuNQW4Ty9BZNJ40_qwq6AxklInN2Gy5psWITbcOFDo2qNd4aiPKsu1XA4r5rnm3r-HT_A_eI-34aP5zKXOzCoFpW7ByGurc8VM8J67NvFInWJLqzz3liFgkAAz_vFlss2l4hsfAAYly00JEJDNtCQPIA3hIdtS8oD3jxY1CeyIytS4Bw4yYQeVd1SJcqbWHBR5qXxPtFxAHs9LGRHnFbyDBMBPNm-RrJCtiJVucWmbVNSUDWO-m6Lwu1IMjolQLk2gBc9LM86__uE7v97LI_hMiJTHo6nBw_gSkqbhIx_Yg8G63rjHsJF83U9X9WPun0WwvF5Y_QnJQJoTQ
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=Machine+learning+based+prediction+of+carbon+concentration+in+carburized+steel&rft.jtitle=Scientific+reports&rft.au=Zhang%2C+Yingtao&rft.au=Tang%2C+Zirong&rft.au=Yin%2C+Qing&rft.au=Wang%2C+Yanjie&rft.date=2025-09-29&rft.pub=Nature+Publishing+Group+UK&rft.eissn=2045-2322&rft.volume=15&rft.issue=1&rft_id=info:doi/10.1038%2Fs41598-025-18531-8&rft.externalDocID=10_1038_s41598_025_18531_8
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon