Online quantitative monitoring of milling cutter health condition based on deep convolutional autoencoder

The health condition of milling cutters (HCOMC) could heavily affect workpiece quality. However, it is extremely difficult to be quantified online. To solve this problem, an online quantitative monitoring method (OQM) is proposed based on a deep convolutional autoencoder (CAE). In this method, a hea...

Full description

Saved in:

Bibliographic Details
Published in:	International journal of advanced manufacturing technology Vol. 125; no. 9-10; pp. 4739 - 4752
Main Authors:	Lei, Yuncong, Li, Changgen, Guo, Liang, Gao, Hongli, Liang, Junhua, Sun, Yi, He, Jigang
Format:	Journal Article
Language:	English
Published:	London Springer London 01.04.2023 Springer Nature B.V
Subjects:	Adhesive wear Advanced manufacturing technologies CAE) and Design Classification Computer-Aided Engineering (CAD Decision trees Engineering Fault diagnosis Industrial and Production Engineering Manufacturing Mechanical Engineering Media Management Methods Milling cutters Original Article Representations Sensors Tool changing Wear Workpieces Milling cutters Online quantitative monitoring Convolutional autoencoder Fusion metric Health indicator
ISSN:	0268-3768, 1433-3015
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	The health condition of milling cutters (HCOMC) could heavily affect workpiece quality. However, it is extremely difficult to be quantified online. To solve this problem, an online quantitative monitoring method (OQM) is proposed based on a deep convolutional autoencoder (CAE). In this method, a health indicator (HI) is constructed for fast HCOMC monitoring. The OQM is composed of two parts, offline training and online monitoring. In the offline stage, the multi-sensor monitoring data that record in the cutter normal wear stage (named normal wear data, NWD) are selected from a subsampled life testing dataset to train a deep CAE. In the online stage, each monitoring data segment (MDS) is directly input into the trained CAE to obtain deep representations. Then, the HI is constructed by the mean square error (MSE) between the MDS and the deep representations to monitor the HCOMC. It is called convolutional-autoencoder-reconstruction-error-based health indicator (CARE-HI). In addition to the above-mentioned method, a new metric named isometric fusion metric (IFM) is also designed to assess HI. IFM is able to address the uneven problem of property contribution when using some widely used HI metrics. In the experiment, 28 milling cutters were subjected to cutting experiments under different working conditions. The experimental result demonstrates that the proposed OQM can efficiently improve feature quality and precisely monitor HCOMC. It also illustrates that the CARE-HI outperformed some existing ones in five metric dimensions. Therefore, the proposed CARE-HI can provide more accurate guidance for tool changing in machining.
AbstractList	The health condition of milling cutters (HCOMC) could heavily affect workpiece quality. However, it is extremely difficult to be quantified online. To solve this problem, an online quantitative monitoring method (OQM) is proposed based on a deep convolutional autoencoder (CAE). In this method, a health indicator (HI) is constructed for fast HCOMC monitoring. The OQM is composed of two parts, offline training and online monitoring. In the offline stage, the multi-sensor monitoring data that record in the cutter normal wear stage (named normal wear data, NWD) are selected from a subsampled life testing dataset to train a deep CAE. In the online stage, each monitoring data segment (MDS) is directly input into the trained CAE to obtain deep representations. Then, the HI is constructed by the mean square error (MSE) between the MDS and the deep representations to monitor the HCOMC. It is called convolutional-autoencoder-reconstruction-error-based health indicator (CARE-HI). In addition to the above-mentioned method, a new metric named isometric fusion metric (IFM) is also designed to assess HI. IFM is able to address the uneven problem of property contribution when using some widely used HI metrics. In the experiment, 28 milling cutters were subjected to cutting experiments under different working conditions. The experimental result demonstrates that the proposed OQM can efficiently improve feature quality and precisely monitor HCOMC. It also illustrates that the CARE-HI outperformed some existing ones in five metric dimensions. Therefore, the proposed CARE-HI can provide more accurate guidance for tool changing in machining.
Author	He, Jigang Gao, Hongli Liang, Junhua Lei, Yuncong Guo, Liang Sun, Yi Li, Changgen
Author_xml	– sequence: 1 givenname: Yuncong surname: Lei fullname: Lei, Yuncong organization: School of Mechanical Engineering, Southwest Jiaotong University, Engineering Research Center of Advanced Drive Energy saving Technologies, Ministry of Education, Southwest Jiaotong University – sequence: 2 givenname: Changgen surname: Li fullname: Li, Changgen organization: School of Mechanical Engineering, Southwest Jiaotong University, Engineering Research Center of Advanced Drive Energy saving Technologies, Ministry of Education, Southwest Jiaotong University – sequence: 3 givenname: Liang surname: Guo fullname: Guo, Liang email: guoliang@swjtu.edu.cn organization: School of Mechanical Engineering, Southwest Jiaotong University, Engineering Research Center of Advanced Drive Energy saving Technologies, Ministry of Education, Southwest Jiaotong University – sequence: 4 givenname: Hongli surname: Gao fullname: Gao, Hongli organization: School of Mechanical Engineering, Southwest Jiaotong University, Engineering Research Center of Advanced Drive Energy saving Technologies, Ministry of Education, Southwest Jiaotong University – sequence: 5 givenname: Junhua surname: Liang fullname: Liang, Junhua organization: School of Mechanical Engineering, Southwest Jiaotong University, Engineering Research Center of Advanced Drive Energy saving Technologies, Ministry of Education, Southwest Jiaotong University – sequence: 6 givenname: Yi surname: Sun fullname: Sun, Yi organization: School of Mechanical Engineering, Southwest Jiaotong University, Engineering Research Center of Advanced Drive Energy saving Technologies, Ministry of Education, Southwest Jiaotong University – sequence: 7 givenname: Jigang surname: He fullname: He, Jigang organization: School of Mechanical Engineering, Southwest Jiaotong University, Engineering Research Center of Advanced Drive Energy saving Technologies, Ministry of Education, Southwest Jiaotong University
BookMark	eNp9kE1LAzEQhoNUsFb_gKeA59VJ0s2mRyl-QaEXPYc0O9HINqlJtuC_d9cKgoeeMmTeZ5h5zskkxICEXDG4YQDNbQZgDVTARcVgIUWlTsiUzYWoBLB6QqbApapEI9UZOc_5Y4hLJtWU-HXofED62ZtQfDHF75FuY_AlJh_eaHR067tuLG1fCib6jqYr79TG0PriY6Abk7GlQ9Ei7sb_fez6sWM6avoSMdjYYrogp850GS9_3xl5fbh_WT5Vq_Xj8_JuVVmueKnmG4aicco5tPPWCCkAbM0QBTonHfKaz6GFGhfKQMu4kRIXyEwNarOxTStm5Powd5fiZ4-56I_Yp2GZrHmjFnWtBmFDSh1SNsWcEzptf66PoSTjO81Aj2L1QawexOofsVoNKP-H7pLfmvR1HBIHKO9Gr5j-tjpCfQNQX5AL
CitedBy_id	crossref_primary_10_1016_j_engappai_2025_110059 crossref_primary_10_1109_TIM_2024_3374301 crossref_primary_10_1007_s10845_024_02459_3 crossref_primary_10_1016_j_jmrt_2025_03_103
Cites_doi	10.1016/j.ymssp.2017.11.021 10.1016/j.ymssp.2017.11.016 10.1016/j.isatra.2017.03.024 10.1016/j.measurement.2020.108153 10.1016/j.wear.2020.203479 10.1088/1757-899X/520/1/012009 10.1016/j.ijmachtools.2006.11.007 10.1109/TII.2021.3118994 10.1007/s00170-018-3157-5 10.1016/S0890-6955(97)00023-0 10.1016/j.ymssp.2022.108904 10.1109/TIE.2014.2327917 10.1016/S0924-0136(01)00878-0 10.3390/ma11101977 10.1016/j.jmrt.2019.10.031 10.1016/j.ymssp.2021.108573 10.3901/JME.2019.11.001 10.1016/j.measurement.2021.110072 10.3390/s22218416 10.1007/s00170-020-05354-2 10.1007/s00170-020-05890-x 10.1016/j.measurement.2016.05.022 10.1016/S0166-3615(96)00075-9 10.1016/j.precisioneng.2021.07.019 10.1007/s10845-017-1334-2 10.1109/TIE.2021.3139202 10.1016/j.neucom.2020.07.088 10.1016/S0890-6955(03)00023-3 10.1016/j.ijmachtools.2014.10.011 10.1016/j.jmsy.2021.03.025 10.1016/j.ymssp.2005.09.012 10.1016/j.wear.2022.204468
ContentType	Journal Article
Copyright	The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. 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.
Copyright_xml	– notice: The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. 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.
DBID	AAYXX CITATION 8FE 8FG ABJCF AFKRA BENPR BGLVJ CCPQU DWQXO HCIFZ L6V M7S PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS
DOI	10.1007/s00170-023-10963-8
DatabaseName	CrossRef ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central UK/Ireland ProQuest Central Technology Collection (via ProQuest SciTech Premium Collection) ProQuest One ProQuest Central Korea SciTech Premium Collection (via ProQuest) ProQuest Engineering Collection Engineering Database Proquest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering collection
DatabaseTitle	CrossRef Engineering Database Technology Collection ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest One Academic UKI Edition ProQuest Central Korea Materials Science & Engineering Collection ProQuest One Academic ProQuest Central (New) Engineering Collection ProQuest One Academic (New)
DatabaseTitleList	Engineering Database
Database_xml	– sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1433-3015
EndPage	4752
ExternalDocumentID	10_1007_s00170_023_10963_8
GrantInformation_xml	– fundername: Local Development Foundation guided by the Central Government grantid: 2020ZYD012 – fundername: Fundamental Research Funds for the Central Universities grantid: 2682022CX058 funderid: http://dx.doi.org/10.13039/501100012226 – fundername: National Natural Science Foundation of China grantid: 51905452 funderid: http://dx.doi.org/10.13039/501100001809 – fundername: National Science Fund for Distinguished Young Scholars grantid: 52105562 funderid: http://dx.doi.org/10.13039/501100014219
GroupedDBID	-5B -5G -BR -EM -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 AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDZT ABECU ABFTD ABFTV ABHQN ABJCF ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTAH ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACUHS ACZOJ ADHHG ADHIR ADINQ ADKNI ADKPE ADMLS ADQRH ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARCEE ARMRJ ASPBG AVWKF AXYYD 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 GQ6 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 PT4 PT5 PTHSS QOK QOS R4E R89 R9I RHV RIG 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 Z5O Z7R Z7S Z7V Z7W Z7X Z7Y Z7Z Z81 Z83 Z85 Z86 Z88 Z8M Z8N Z8P Z8Q Z8R Z8S Z8T Z8U Z8V Z8W Z8Z Z92 ZMTXR ZY4 _50 ~8M ~A9 ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB DWQXO PKEHL PQEST PQQKQ PQUKI PRINS
ID	FETCH-LOGICAL-c282t-4b1e37f8ffec4da36300c51ee3eff6fe25240d05e98a0d12a66e9e1a508bbc7d3
IEDL.DBID	M7S
ISICitedReferencesCount	4
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000936551800004&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	Wed Nov 05 00:59:16 EST 2025 Sat Nov 29 03:17:21 EST 2025 Tue Nov 18 21:11:09 EST 2025 Fri Feb 21 02:43:46 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	9-10
Keywords	Milling cutters Online quantitative monitoring Convolutional autoencoder Fusion metric Health indicator
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c282t-4b1e37f8ffec4da36300c51ee3eff6fe25240d05e98a0d12a66e9e1a508bbc7d3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
PQID	2789558007
PQPubID	2044010
PageCount	14
ParticipantIDs	proquest_journals_2789558007 crossref_citationtrail_10_1007_s00170_023_10963_8 crossref_primary_10_1007_s00170_023_10963_8 springer_journals_10_1007_s00170_023_10963_8
PublicationCentury	2000
PublicationDate	20230400 2023-04-00 20230401
PublicationDateYYYYMMDD	2023-04-01
PublicationDate_xml	– month: 4 year: 2023 text: 20230400
PublicationDecade	2020
PublicationPlace	London
PublicationPlace_xml	– name: London – name: Heidelberg
PublicationTitle	International journal of advanced manufacturing technology
PublicationTitleAbbrev	Int J Adv Manuf Technol
PublicationYear	2023
Publisher	Springer London Springer Nature B.V
Publisher_xml	– name: Springer London – name: Springer Nature B.V
References	Kong, Chen, Li (CR6) 2018; 104 Li, Liu, Yue, Liu, Zhang, Li, Liang, Wang (CR30) 2021; 185 Sun, Yeh (CR15) 2018; 11 Kurada, Bradley (CR5) 1997; 34 Ou, Li, Huang, Yang (CR26) 2021; 167 Abu-Mahfouz (CR20) 2003; 43 Javed, Gouriveau, Zerhouni, Nectoux (CR33) 2015; 62 Lanzetta (CR9) 2001; 119 CR12 Chen, Luo (CR16) 2020; 109 Nouri, Fussell, Ziniti, Linder (CR29) 2015; 89 Jin, Siegel, Weiss, Gamel, Wang, Lee, Ni (CR2) 2016; 3 Jindal (CR11) 2012; 11 Twardowski, Tabaszewski, Wiciak-Pikuła, Felusiak-Czyryca (CR27) 2021; 72 Schwenzer, Miura, Bergs (CR22) 2019; 520 You, Gao, Guo, Liu, Li (CR17) 2020; 460 Bhat, Dutta, Pal, Pal (CR21) 2016; 90 Lei, Li, Guo, Li, Yan, Lin (CR35) 2018; 104 Guo, Yu, Liu, Gao, Chen (CR3) 2022; 71 You, Gao, Li, Guo, Liu, Li (CR19) 2022; 69 Lim, Son, Wong, Rahman (CR10) 2007; 47 Li, Wang, He, Hao, Yang, Wei (CR24) 2020; 110 Jain, Lad (CR7) 2019; 30 Jardine, Lin, Banjevic (CR1) 2006; 20 Hanachi, Yu, Kim, Liu, Mechefske (CR4) 2019; 101 Mohanraj, Shankar, Rajasekar, Sakthivel, Pramanik (CR8) 2020; 9 Yang, Duan, Li, Hu, Liang, Shi (CR28) 2022; 22 Li, Hao, Dai, Yang (CR23) 2019; 55 Karandikar, Schmitz, Smith (CR25) 2021; 59 Guo, Yu, Gao, Feng, Liu (CR31) 2022; 18 Karthik, Chandra, Ramamoorthy, Das (CR13) 1997; 37 Jiao, Zhao, Lin, Liang (CR32) 2020; 417 Guo, Yu, Duan, Gao, Zhang (CR34) 2022; 167 You, Gao, Guo, Liu, Li, Li (CR18) 2022; 171 Yu, Lin, Dai, Zhu (CR14) 2017; 69 A Karthik (10963_CR13) 1997; 37 S Chen (10963_CR16) 2020; 109 T Mohanraj (10963_CR8) 2020; 9 Z You (10963_CR19) 2022; 69 X Li (10963_CR30) 2021; 185 L Guo (10963_CR34) 2022; 167 X Jin (10963_CR2) 2016; 3 S Kurada (10963_CR5) 1997; 34 X Yu (10963_CR14) 2017; 69 J Ou (10963_CR26) 2021; 167 H Lim (10963_CR10) 2007; 47 J Yang (10963_CR28) 2022; 22 M Nouri (10963_CR29) 2015; 89 P Twardowski (10963_CR27) 2021; 72 K Javed (10963_CR33) 2015; 62 J Karandikar (10963_CR25) 2021; 59 Y Lei (10963_CR35) 2018; 104 L Guo (10963_CR3) 2022; 71 A Jardine (10963_CR1) 2006; 20 A Jain (10963_CR7) 2019; 30 10963_CR12 M Schwenzer (10963_CR22) 2019; 520 J Jiao (10963_CR32) 2020; 417 M Lanzetta (10963_CR9) 2001; 119 L Guo (10963_CR31) 2022; 18 I Abu-Mahfouz (10963_CR20) 2003; 43 A Jindal (10963_CR11) 2012; 11 Z You (10963_CR18) 2022; 171 H Li (10963_CR23) 2019; 55 W Sun (10963_CR15) 2018; 11 Z You (10963_CR17) 2020; 460 N Bhat (10963_CR21) 2016; 90 H Hanachi (10963_CR4) 2019; 101 D Kong (10963_CR6) 2018; 104 G Li (10963_CR24) 2020; 110
References_xml	– volume: 104 start-page: 556 year: 2018 end-page: 574 ident: CR6 article-title: Gaussian process regression for tool wear prediction publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2017.11.021 – volume: 104 start-page: 799 year: 2018 end-page: 834 ident: CR35 article-title: Machinery health prognostics: A systematic review from data acquisition to RUL prediction publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2017.11.016 – volume: 69 start-page: 315 year: 2017 end-page: 322 ident: CR14 article-title: Image edge detection based tool condition monitoring with morphological component analysis publication-title: ISA Trans doi: 10.1016/j.isatra.2017.03.024 – volume: 167 year: 2021 ident: CR26 article-title: Intelligent analysis of tool wear state using stacked denoising autoencoder with online sequential-extreme learning machine publication-title: Measurement doi: 10.1016/j.measurement.2020.108153 – volume: 460 year: 2020 ident: CR17 article-title: On-line milling cutter wear monitoring in a wide field-of-view camera publication-title: Wear doi: 10.1016/j.wear.2020.203479 – volume: 520 year: 2019 ident: CR22 article-title: Machine learning for tool wear classification in milling based on force and current sensors publication-title: IOP Conf Ser Mater Sci Eng doi: 10.1088/1757-899X/520/1/012009 – volume: 47 start-page: 1556 issue: 10 year: 2007 end-page: 1562 ident: CR10 article-title: Development and evaluation of an on-machine optical measurement device publication-title: Int J Mach Tools Manuf doi: 10.1016/j.ijmachtools.2006.11.007 – volume: 18 start-page: 5199 issue: 8 year: 2022 end-page: 5208 ident: CR31 article-title: Online remaining useful life prediction of milling cutters based on multisource data and feature learning publication-title: IEEE Trans Ind Informatics doi: 10.1109/TII.2021.3118994 – volume: 101 start-page: 2861 year: 2019 end-page: 2872 ident: CR4 article-title: Hybrid data-driven physics-based model fusion framework for tool wear prediction publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-018-3157-5 – volume: 37 start-page: 1573 issue: 11 year: 1997 end-page: 1581 ident: CR13 article-title: 3D tool wear measurement and visualisation using stereo imaging publication-title: Int J Mach Tools Manuf doi: 10.1016/S0890-6955(97)00023-0 – volume: 171 year: 2022 ident: CR18 article-title: Machine vision based adaptive online condition monitoring for milling cutter under spindle rotation publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2022.108904 – volume: 62 start-page: 647 issue: 1 year: 2015 end-page: 656 ident: CR33 article-title: Enabling health monitoring approach based on vibration data for accurate prognostics publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2014.2327917 – volume: 119 start-page: 73 issue: 1–3 year: 2001 end-page: 82 ident: CR9 article-title: A new flexible high-resolution vision sensor for tool condition monitoring publication-title: J Mater Process Technol doi: 10.1016/S0924-0136(01)00878-0 – ident: CR12 – volume: 11 start-page: 1977 issue: 10 year: 2018 ident: CR15 article-title: Using the machine vision method to develop an on-machine insert condition monitoring system for computer numerical control turning machine tools publication-title: Materials doi: 10.3390/ma11101977 – volume: 9 start-page: 1032 issue: 1 year: 2020 end-page: 1042 ident: CR8 article-title: Tool condition monitoring techniques in milling process-a review publication-title: J Mater Res Technol doi: 10.1016/j.jmrt.2019.10.031 – volume: 167 year: 2022 ident: CR34 article-title: An unsupervised feature learning based health indicator construction method for performance assessment of machines publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2021.108573 – volume: 55 start-page: 1 issue: 14 year: 2019 end-page: 10 ident: CR23 article-title: Wear status recognition for milling cutter based on compressed sensing and noise stacking sparse auto-encoder publication-title: J Mech Eng doi: 10.3901/JME.2019.11.001 – volume: 185 year: 2021 ident: CR30 article-title: A data-driven approach for tool wear recognition and quantitative prediction based on radar map feature fusion publication-title: Measurement doi: 10.1016/j.measurement.2021.110072 – volume: 22 start-page: 8416 year: 2022 ident: CR28 article-title: Tool wear monitoring in milling based on fine-grained image classification of machined surface images publication-title: Sensors doi: 10.3390/s22218416 – volume: 109 start-page: 823 year: 2020 end-page: 839 ident: CR16 article-title: Study of using cutting chip color to the tool wear prediction publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-020-05354-2 – volume: 110 start-page: 511 year: 2020 end-page: 522 ident: CR24 article-title: Tool wear state recognition based on gradient boosting decision tree and hybrid classification RBM publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-020-05890-x – volume: 90 start-page: 500 year: 2016 end-page: 509 ident: CR21 article-title: Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images publication-title: Measurement doi: 10.1016/j.measurement.2016.05.022 – volume: 3 start-page: 10 year: 2016 ident: CR2 article-title: The present status and future growth of maintenance in US manufacturing: results from a pilot survey publication-title: Manuf Rev – volume: 34 start-page: 55 issue: 1 year: 1997 end-page: 72 ident: CR5 article-title: A review of machine vision sensors for tool condition monitoring publication-title: Comput Ind doi: 10.1016/S0166-3615(96)00075-9 – volume: 72 start-page: 738 year: 2021 end-page: 744 ident: CR27 article-title: Identification of tool wear using acoustic emission signal and machine learning methods publication-title: Precis Eng doi: 10.1016/j.precisioneng.2021.07.019 – volume: 30 start-page: 1423 year: 2019 end-page: 1436 ident: CR7 article-title: A novel integrated tool condition monitoring system publication-title: J Intell Manuf doi: 10.1007/s10845-017-1334-2 – volume: 69 start-page: 13656 issue: 12 year: 2022 end-page: 13664 ident: CR19 article-title: Multiple activation functions and data augmentation based light weight network for in-situ tool condition monitoring publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2021.3139202 – volume: 71 start-page: 1 year: 2022 end-page: 10 ident: CR3 article-title: Reconstruction domain adaptation transfer network for partial transfer learning of machinery fault diagnostics publication-title: IEEE Trans Instrum Meas – volume: 11 start-page: 43 issue: 1 year: 2012 end-page: 54 ident: CR11 article-title: Analysis of tool wear rate in drilling operation using scanning electron microscope (SEM) publication-title: J Miner Mater Charact Eng – volume: 417 start-page: 36 year: 2020 end-page: 63 ident: CR32 article-title: A comprehensive review on convolutional neural network in machine fault diagnosis publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.07.088 – volume: 43 start-page: 707 issue: 7 year: 2003 end-page: 720 ident: CR20 article-title: Drilling wear detection and classification using vibration signals and artificial neural network publication-title: Int J Mach Tools Manuf doi: 10.1016/S0890-6955(03)00023-3 – volume: 89 start-page: 1 year: 2015 end-page: 13 ident: CR29 article-title: Real-time tool wear monitoring in milling using a cutting condition independent method publication-title: Int J Mach Tools Manuf doi: 10.1016/j.ijmachtools.2014.10.011 – volume: 59 start-page: 522 year: 2021 end-page: 534 ident: CR25 article-title: Physics-guided logistic classification for tool life modeling and process parameter optimization in machining publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2021.03.025 – volume: 20 start-page: 1483 issue: 7 year: 2006 end-page: 1510 ident: CR1 article-title: A review on machinery diagnostics and prognostics implementing condition-based maintenance publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2005.09.012 – volume: 101 start-page: 2861 year: 2019 ident: 10963_CR4 publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-018-3157-5 – volume: 43 start-page: 707 issue: 7 year: 2003 ident: 10963_CR20 publication-title: Int J Mach Tools Manuf doi: 10.1016/S0890-6955(03)00023-3 – volume: 167 year: 2022 ident: 10963_CR34 publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2021.108573 – volume: 104 start-page: 556 year: 2018 ident: 10963_CR6 publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2017.11.021 – volume: 34 start-page: 55 issue: 1 year: 1997 ident: 10963_CR5 publication-title: Comput Ind doi: 10.1016/S0166-3615(96)00075-9 – volume: 30 start-page: 1423 year: 2019 ident: 10963_CR7 publication-title: J Intell Manuf doi: 10.1007/s10845-017-1334-2 – volume: 69 start-page: 315 year: 2017 ident: 10963_CR14 publication-title: ISA Trans doi: 10.1016/j.isatra.2017.03.024 – volume: 71 start-page: 1 year: 2022 ident: 10963_CR3 publication-title: IEEE Trans Instrum Meas – volume: 22 start-page: 8416 year: 2022 ident: 10963_CR28 publication-title: Sensors doi: 10.3390/s22218416 – volume: 3 start-page: 10 year: 2016 ident: 10963_CR2 publication-title: Manuf Rev – volume: 11 start-page: 1977 issue: 10 year: 2018 ident: 10963_CR15 publication-title: Materials doi: 10.3390/ma11101977 – volume: 20 start-page: 1483 issue: 7 year: 2006 ident: 10963_CR1 publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2005.09.012 – volume: 110 start-page: 511 year: 2020 ident: 10963_CR24 publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-020-05890-x – volume: 69 start-page: 13656 issue: 12 year: 2022 ident: 10963_CR19 publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2021.3139202 – volume: 104 start-page: 799 year: 2018 ident: 10963_CR35 publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2017.11.016 – volume: 18 start-page: 5199 issue: 8 year: 2022 ident: 10963_CR31 publication-title: IEEE Trans Ind Informatics doi: 10.1109/TII.2021.3118994 – volume: 9 start-page: 1032 issue: 1 year: 2020 ident: 10963_CR8 publication-title: J Mater Res Technol doi: 10.1016/j.jmrt.2019.10.031 – volume: 520 year: 2019 ident: 10963_CR22 publication-title: IOP Conf Ser Mater Sci Eng doi: 10.1088/1757-899X/520/1/012009 – volume: 72 start-page: 738 year: 2021 ident: 10963_CR27 publication-title: Precis Eng doi: 10.1016/j.precisioneng.2021.07.019 – ident: 10963_CR12 doi: 10.1016/j.wear.2022.204468 – volume: 62 start-page: 647 issue: 1 year: 2015 ident: 10963_CR33 publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2014.2327917 – volume: 417 start-page: 36 year: 2020 ident: 10963_CR32 publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.07.088 – volume: 89 start-page: 1 year: 2015 ident: 10963_CR29 publication-title: Int J Mach Tools Manuf doi: 10.1016/j.ijmachtools.2014.10.011 – volume: 47 start-page: 1556 issue: 10 year: 2007 ident: 10963_CR10 publication-title: Int J Mach Tools Manuf doi: 10.1016/j.ijmachtools.2006.11.007 – volume: 11 start-page: 43 issue: 1 year: 2012 ident: 10963_CR11 publication-title: J Miner Mater Charact Eng – volume: 37 start-page: 1573 issue: 11 year: 1997 ident: 10963_CR13 publication-title: Int J Mach Tools Manuf doi: 10.1016/S0890-6955(97)00023-0 – volume: 55 start-page: 1 issue: 14 year: 2019 ident: 10963_CR23 publication-title: J Mech Eng doi: 10.3901/JME.2019.11.001 – volume: 460 year: 2020 ident: 10963_CR17 publication-title: Wear doi: 10.1016/j.wear.2020.203479 – volume: 109 start-page: 823 year: 2020 ident: 10963_CR16 publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-020-05354-2 – volume: 90 start-page: 500 year: 2016 ident: 10963_CR21 publication-title: Measurement doi: 10.1016/j.measurement.2016.05.022 – volume: 167 year: 2021 ident: 10963_CR26 publication-title: Measurement doi: 10.1016/j.measurement.2020.108153 – volume: 185 year: 2021 ident: 10963_CR30 publication-title: Measurement doi: 10.1016/j.measurement.2021.110072 – volume: 119 start-page: 73 issue: 1–3 year: 2001 ident: 10963_CR9 publication-title: J Mater Process Technol doi: 10.1016/S0924-0136(01)00878-0 – volume: 171 year: 2022 ident: 10963_CR18 publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2022.108904 – volume: 59 start-page: 522 year: 2021 ident: 10963_CR25 publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2021.03.025
SSID	ssj0016168 ssib034539549 ssib019759004 ssib029851711
Score	2.389213
Snippet	The health condition of milling cutters (HCOMC) could heavily affect workpiece quality. However, it is extremely difficult to be quantified online. To solve...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	4739
SubjectTerms	Adhesive wear Advanced manufacturing technologies CAE) and Design Classification Computer-Aided Engineering (CAD Decision trees Engineering Fault diagnosis Industrial and Production Engineering Manufacturing Mechanical Engineering Media Management Methods Milling cutters Original Article Representations Sensors Tool changing Wear Workpieces
SummonAdditionalLinks	– databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07T8MwELagMMDAG1EoyAMbRIqTOHZGhKiYKsRL3SInPkuVIC1t2t_P2XkUECDBZiVOopzP99D5vo-Qcy24wmhIeDpiEhMUwb0k98HzswiMMlIqLR3ZhBgM5HCY3NVNYbPmtHtTknSWum12c1AvHvoYNB2oNp5cJWvo7qQlbLh_eG60iCXCMmG2WhYkln5-qcVhxMOqtlXXGmLmGuYwGZF2u8m6teb7b352X8uY9EsZ1Xmn_vb__muHbNXRKL2q1GeXrECxRzY_YBTuk1EFRkrf5qpwDWloHumrswR2Ah0bapmL7DB3pNe0aq2kmGhrdx6MWk-pKQ40wMReX9T6jp9W83JssTQ1TA_IU__m8frWq_kZvBwTtdKLMgahMNIePIm0Ci16V84ZQAjGxAYCjuGC9jkkUvmaBSqOIQGmMCbMslzo8JB0inEBR4TmATAItG-YNBGAQr8pQKs4YwFICWGXsEbsaV6Dl1sOjZe0hV12YkxRjKkTYyq75KJ9ZlJBd_w6u9esZlpv41lq24Q5x5hadMlls3rL2z-_7fhv00_IhqWxr04E9UinnM7hlKzni3I0m5459X4H7C7xNA priority: 102 providerName: Springer Nature
Title	Online quantitative monitoring of milling cutter health condition based on deep convolutional autoencoder
URI	https://link.springer.com/article/10.1007/s00170-023-10963-8 https://www.proquest.com/docview/2789558007
Volume	125
WOSCitedRecordID	wos000936551800004&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 Contemporary 1997-Present 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/eLvHCXMwpV1Lb9QwEB5BywEOBQqIbUvlA7fWIs7LzglR1IrTqmqh6i1y7LFUie5u99Hfz4zj7LZI9MIlshLHUTTjmbE9830An72uLEVDWvpSGVqg6Eo2LkOZdSUGG4yx3kSyCT0em-vr5jxtuC1SWuVgE6Oh9lPHe-RfuGKzqii80V9nd5JZo_h0NVFoPIdtRklQMXXvctAn1WjmxFzrW94wEf1Gn4uyKvpTrnTqUKtYOkfLEsMTz6Qim1hqF4FmJHk4MlyktNI8dmSb6PSvA9Xop85e_-8fvoGdFKGKb71KvYVnONmFVw9wC9_BTQ9QKu5WdhKL1MhkittoHbiDmAbBbEbcdJEIW_TlloIW3z7miAn2nl5QwyPO-P59mgP0abtaThlf0-P8Pfw6O_35_YdMnA3S0eJtKctOYaGD4WSU0tuCEb1cpRALDKEOmFcUQviswsbYzKvc1jU2qCzFiV3ntC8-wNZkOsGPIFyOCnOfBWVCiWjJl2r0tu5UjsZgMQI1CKB1CdCceTV-t2so5ii0loTWRqG1ZgRH63dmPZzHk70PBkm1aWov2o2YRnA8yHrz-N-j7T092j68ZCr7PivoALaW8xV-ghfufnmzmB_C9snp-PziMCo4XS8ur_4AJ9X-Gw
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VgkQ58EZdKOADnCBq7DzsHBBCQNWqZYVEkXoLjj2WKsFmu48i_hS_kRkn2QUkeuuBW5Q4jhJ_84pn5gN45nVhyRvSic-loQBFF0nlUkzSJsdggzHWm0g2ocdjc3JSfdyAn0MtDKdVDjoxKmrfOv5HvssVm0VB7o1-PT1LmDWKd1cHCo0OFof44zuFbPNXB-9ofZ8rtff--O1-0rMKJI7Ci0WSNxIzHQynS-TeZtxzyhUSMcMQyoCqICPn0wIrY1MvlS1LrFBa8mSaxmmf0bxX4Cq5EaqKqYKfBvzKSjMH5wrfqmLi-7X8ZHmRdbtq_S5HKWOpHoVBhgXd9EU9sbQvNrZJyKKSoiQhScyfhnPtDf-1gRvt4t6t_-2L3oabvQcu3nQicwc2cHIXbvzWl_EenHYNWMXZ0k5iER6ZBPEtaj8eINogmK2JD10k-hZdOalwLWcAENQFewde0IFHnPL5817G6dF2uWi5f6jH2X34fCnv-gA2J-0Et0E4hRKVT4M0IUe05Cto9LZspEJjMBuBHBa8dn3DduYN-VqvWk1HkNQEkjqCpDYjeLG6Z9q1K7lw9M6AjLpXXfN6DYsRvBywtb7879keXjzbU7i-f_zhqD46GB8-gi3F0I4ZUDuwuZgt8TFcc-eL0_nsSRQqAV8uG3O_AGp0WsY
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT-MwEB5BQQgOy2sRXV4-cIOIOC87xxW7FQhUIfEQt8iJxxISm5aS9vfjcZIWEKyEuFmJEyf22DOjmfk-gEMtYmWtIeHpiEvroIjYSwsfPT-P0CgjpdLSkU2Ifl_e36dXr6r4XbZ7G5KsaxoIpamsTobanEwL3xzsi2f1jT1GrAh5ch4WIkqkJ3_9-q6VKJ4KYsWcSlyQEhX9TKLDKA7rOFcTd0i4K56zjomkrSebMpuPx3yrymb26buQqtNUvdXv_-Ma_GisVPa7Fqt1mMNyA1ZeYRduwkMNUsqexqp0hWr22GT_3AlBHdjAMGI0ombhyLBZXXLJ7FdolyfGSINqZhsacUjXJ80-sEOrcTUgjE2No59w2_t7c3rmNbwNXmEduMqLco6hMJISUiKtQkL1KmKOGKIxicEgtmaE9mNMpfI1D1SSYIpcWVsxzwuhwy3olIMSt4EVAXIMtG-4NBGisvpUoFZJzgOUEsMu8HYJsqIBNSdujcdsCsfspjGz05i5acxkF46mzwxrSI__9t5tVzZrtvdzRuXDcWxtbdGF43YlZ7c_f9uvr3U_gKWrP73s8rx_sQPLxHRfJw3tQqcajXEPFotJ9fA82ndS_wK2Ovz8
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=Online+quantitative+monitoring+of+milling+cutter+health+condition+based+on+deep+convolutional+autoencoder&rft.jtitle=International+journal+of+advanced+manufacturing+technology&rft.au=Lei%2C+Yuncong&rft.au=Li%2C+Changgen&rft.au=Guo%2C+Liang&rft.au=Gao%2C+Hongli&rft.date=2023-04-01&rft.pub=Springer+Nature+B.V&rft.issn=0268-3768&rft.eissn=1433-3015&rft.volume=125&rft.issue=9-10&rft.spage=4739&rft.epage=4752&rft_id=info:doi/10.1007%2Fs00170-023-10963-8
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