Data-Driven Energy Modeling of Machining Centers Through Automata Learning

The paper addresses the problem of estimating the energy consumed by production resources in manufacturing so that alternative process designs can be compared in terms of energy expenditure. In particular, the proposed methodology focuses on Computer Numerical Controlled (CNC) machining centers. Cla...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on automation science and engineering Vol. 22; pp. 5769 - 5780
Main Authors: Lestingi, Livia, Frigerio, Nicla, Bersani, Marcello M., Matta, Andrea, Rossi, Matteo
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
automata learning
Biological system modeling
data-driven modeling
Energy consumption
Energy modeling
Machining
Numerical models
Predictive models
Task analysis
Training
ISSN:1545-5955, 1558-3783
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract The paper addresses the problem of estimating the energy consumed by production resources in manufacturing so that alternative process designs can be compared in terms of energy expenditure. In particular, the proposed methodology focuses on Computer Numerical Controlled (CNC) machining centers. Classical approaches to energy modeling require high expertise and large development effort since, for example, data acquisition is resource-specific and must be repeated frequently to avoid obsolescence. An automated and flexible data-driven methodology is designed in this work. A data-driven method is employed to learn a hybrid and stochastic model of a CNC machining center's energetic behavior. The learned model is used to provide offline energy consumption estimates of simulated part-programs before the actual execution of the cutting. Numerical results show the performance of the proposed method on a set of case studies. The methodology is also applied to a real industrial application, including data collected during machine production. Note to Practitioners-This article provides a flexible and autonomous data-driven approach to building models representing the energetic behavior of production resources, particularly CNC machining centers. The learned models can predict machine energy consumption while executing complex part-programs. The algorithm uses data that are commonly acquired by contemporary machine monitoring systems and does not require ad-hoc experimental tests for training. Specifically, it requires the spindle rotary speed signal, part load/unload signal, and spindle (or machine) power signal during the learning phase, whilst the estimation phase uses only the load/unload and spindle speed simulated signals.
AbstractList The paper addresses the problem of estimating the energy consumed by production resources in manufacturing so that alternative process designs can be compared in terms of energy expenditure. In particular, the proposed methodology focuses on Computer Numerical Controlled (CNC) machining centers. Classical approaches to energy modeling require high expertise and large development effort since, for example, data acquisition is resource-specific and must be repeated frequently to avoid obsolescence. An automated and flexible data-driven methodology is designed in this work. A data-driven method is employed to learn a hybrid and stochastic model of a CNC machining center's energetic behavior. The learned model is used to provide offline energy consumption estimates of simulated part-programs before the actual execution of the cutting. Numerical results show the performance of the proposed method on a set of case studies. The methodology is also applied to a real industrial application, including data collected during machine production. Note to Practitioners-This article provides a flexible and autonomous data-driven approach to building models representing the energetic behavior of production resources, particularly CNC machining centers. The learned models can predict machine energy consumption while executing complex part-programs. The algorithm uses data that are commonly acquired by contemporary machine monitoring systems and does not require ad-hoc experimental tests for training. Specifically, it requires the spindle rotary speed signal, part load/unload signal, and spindle (or machine) power signal during the learning phase, whilst the estimation phase uses only the load/unload and spindle speed simulated signals.
Author Lestingi, Livia
Bersani, Marcello M.
Frigerio, Nicla
Matta, Andrea
Rossi, Matteo
Author_xml – sequence: 1
  givenname: Livia
  orcidid: 0000-0001-8724-1541
  surname: Lestingi
  fullname: Lestingi, Livia
  email: livia.lestingi@polimi.it
  organization: Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
– sequence: 2
  givenname: Nicla
  orcidid: 0000-0001-8146-9772
  surname: Frigerio
  fullname: Frigerio, Nicla
  organization: Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy
– sequence: 3
  givenname: Marcello M.
  orcidid: 0000-0001-5137-940X
  surname: Bersani
  fullname: Bersani, Marcello M.
  organization: Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
– sequence: 4
  givenname: Andrea
  orcidid: 0000-0003-3902-2007
  surname: Matta
  fullname: Matta, Andrea
  organization: Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy
– sequence: 5
  givenname: Matteo
  orcidid: 0000-0002-9193-9560
  surname: Rossi
  fullname: Rossi, Matteo
  organization: Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy
BookMark eNp9kE1PAjEQQBuDiYD-ABMP_QOL_dzdHgkgaiAexPNm2m2hBlrTXUz49-4GDsaDp5lJ3pvDG6FBiMEidE_JhFKiHjfT98WEESYmXHDClbhCQyplmfGi5IN-FzKTSsobNGqaT9KRpSJD9DqHFrJ58t824EWwaXvC61jbvQ9bHB1eg9n50B8zG1qbGrzZpXjc7vD02MZDJ-OVhdQTt-jawb6xd5c5Rh9Pi83sOVu9LV9m01VmOFFtBmVuasgZY9rlMpec15oLZ0BoIpjhTmsqy8JRYzVxQEoKjBU5aFZzojt8jIrzX5Ni0yTrKuNbaH0MbQK_ryip-iRVn6Tqk1SXJJ1J_5hfyR8gnf51Hs6Ot9b-4nNSKKX4D32LbxE
CODEN ITASC7
CitedBy_id crossref_primary_10_1016_j_procir_2024_12_024
Cites_doi 10.1007/s00170-022-10194-3
10.1021/es8016655
10.1016/0890-5401(87)90052-6
10.1016/j.jclepro.2020.123125
10.1016/j.cirpj.2021.07.014
10.1016/j.procir.2022.02.032
10.1115/IMECE2004-62600
10.1016/j.jclepro.2016.04.012
10.1177/0954405414539490
10.1201/9781315140919
10.1109/tase.2023.3315546
10.1007/978-3-642-21455-4_8
10.1016/j.inffus.2019.12.012
10.5937/jaes0-30826
10.1007/978-1-4612-5931-2_7
10.1007/s10009-014-0361-y
10.1137/1.9781611972719.1
10.1214/aoms/1177729394
10.1016/0304-3975(94)00202-T
10.1214/aoms/1177706793
10.1177/1687814016680737
10.1016/j.jclepro.2015.05.093
10.3390/machines11111015
10.1007/s00170-018-2550-4
10.1109/MIS.2022.3215698
10.1016/j.jclepro.2021.129920
10.1007/s100090050010
10.1016/j.procir.2015.08.081
10.1109/ICEMCE60359.2023.10491062
10.1214/lnms/1196285403
10.3390/su132413918
10.1016/j.cirp.2011.03.088
10.1007/s00170-022-09557-7
ContentType Journal Article
DBID 97E
ESBDL
RIA
RIE
AAYXX
CITATION
DOI 10.1109/TASE.2024.3430394
DatabaseName IEEE Xplore (IEEE)
IEEE Xplore Open Access Journals
IEEE All-Society Periodicals Package (ASPP) 1998–Present
IEEE Electronic Library (IEL)
CrossRef
DatabaseTitle CrossRef
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 1558-3783
EndPage 5780
ExternalDocumentID 10_1109_TASE_2024_3430394
10607999
Genre orig-research
GrantInformation_xml – fundername: European Union (EU) Horizon Europe Research and Innovation Program
  grantid: 101092021 (AutoTwin)
GroupedDBID -~X
0R~
29I
4.4
5GY
5VS
6IK
97E
AAJGR
AARMG
AASAJ
AAWTH
ABAZT
ABQJQ
ABVLG
ACGFO
ACGFS
ACIWK
AENEX
AETIX
AGQYO
AGSQL
AHBIQ
AIBXA
AKJIK
AKQYR
ALMA_UNASSIGNED_HOLDINGS
ATWAV
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CS3
DU5
EBS
EJD
ESBDL
F5P
HZ~
H~9
IFIPE
IPLJI
JAVBF
LAI
M43
O9-
OCL
PQQKQ
RIA
RIE
RNS
AAYXX
CITATION
ID FETCH-LOGICAL-c309t-a86cda6222bf656533db34fca4b042c3fbb1587f1ceb0fa081a2276ab2d30b533
IEDL.DBID RIE
ISICitedReferencesCount 0
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001279016800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 1545-5955
IngestDate Tue Nov 18 21:37:43 EST 2025
Sat Nov 29 08:08:55 EST 2025
Wed Aug 27 01:43:35 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed false
IsScholarly true
Language English
License https://creativecommons.org/licenses/by-nc-nd/4.0
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c309t-a86cda6222bf656533db34fca4b042c3fbb1587f1ceb0fa081a2276ab2d30b533
ORCID 0000-0002-9193-9560
0000-0001-8724-1541
0000-0001-8146-9772
0000-0001-5137-940X
0000-0003-3902-2007
OpenAccessLink https://ieeexplore.ieee.org/document/10607999
PageCount 12
ParticipantIDs crossref_citationtrail_10_1109_TASE_2024_3430394
ieee_primary_10607999
crossref_primary_10_1109_TASE_2024_3430394
PublicationCentury 2000
PublicationDate 20250000
2025-00-00
PublicationDateYYYYMMDD 2025-01-01
PublicationDate_xml – year: 2025
  text: 20250000
PublicationDecade 2020
PublicationTitle IEEE transactions on automation science and engineering
PublicationTitleAbbrev TASE
PublicationYear 2025
Publisher IEEE
Publisher_xml – name: IEEE
References ref13
ref12
ref15
ref14
ref31
ref30
ref11
ref33
ref10
ref32
ref2
ref1
ref17
ref16
ref19
ref18
ref24
ref23
ref26
ref25
ref20
ref22
ref21
ref28
ref27
ref29
ref8
ref7
ref9
ref4
ref3
ref6
ref5
References_xml – ident: ref9
  doi: 10.1007/s00170-022-10194-3
– ident: ref12
  doi: 10.1021/es8016655
– ident: ref3
  doi: 10.1016/0890-5401(87)90052-6
– ident: ref26
  doi: 10.1016/j.jclepro.2020.123125
– ident: ref5
  doi: 10.1016/j.cirpj.2021.07.014
– ident: ref22
  doi: 10.1016/j.procir.2022.02.032
– ident: ref10
  doi: 10.1115/IMECE2004-62600
– ident: ref1
  doi: 10.1016/j.jclepro.2016.04.012
– ident: ref30
  doi: 10.1177/0954405414539490
– ident: ref27
  doi: 10.1201/9781315140919
– ident: ref32
  doi: 10.1109/tase.2023.3315546
– ident: ref28
  doi: 10.1007/978-3-642-21455-4_8
– ident: ref4
  doi: 10.1016/j.inffus.2019.12.012
– ident: ref6
  doi: 10.5937/jaes0-30826
– ident: ref23
  doi: 10.1007/978-1-4612-5931-2_7
– ident: ref11
  doi: 10.1007/s10009-014-0361-y
– ident: ref14
  doi: 10.1137/1.9781611972719.1
– ident: ref24
  doi: 10.1214/aoms/1177729394
– ident: ref2
  doi: 10.1016/0304-3975(94)00202-T
– ident: ref21
  doi: 10.1214/aoms/1177706793
– ident: ref31
  doi: 10.1177/1687814016680737
– ident: ref33
  doi: 10.1016/j.jclepro.2015.05.093
– ident: ref29
  doi: 10.3390/machines11111015
– ident: ref25
  doi: 10.1007/s00170-018-2550-4
– ident: ref16
  doi: 10.1109/MIS.2022.3215698
– ident: ref19
  doi: 10.1016/j.jclepro.2021.129920
– ident: ref15
  doi: 10.1007/s100090050010
– ident: ref13
  doi: 10.1016/j.procir.2015.08.081
– ident: ref18
  doi: 10.1109/ICEMCE60359.2023.10491062
– ident: ref8
  doi: 10.1214/lnms/1196285403
– ident: ref7
  doi: 10.3390/su132413918
– ident: ref20
  doi: 10.1016/j.cirp.2011.03.088
– ident: ref17
  doi: 10.1007/s00170-022-09557-7
SSID ssj0024890
Score 2.3893962
Snippet The paper addresses the problem of estimating the energy consumed by production resources in manufacturing so that alternative process designs can be compared...
SourceID crossref
ieee
SourceType Enrichment Source
Index Database
Publisher
StartPage 5769
SubjectTerms automata learning
Biological system modeling
data-driven modeling
Energy consumption
Energy modeling
Machining
Numerical models
Predictive models
Task analysis
Training
Title Data-Driven Energy Modeling of Machining Centers Through Automata Learning
URI https://ieeexplore.ieee.org/document/10607999
Volume 22
WOSCitedRecordID wos001279016800001&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: PRVIEE
  databaseName: IEEE Electronic Library (IEL)
  customDbUrl:
  eissn: 1558-3783
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0024890
  issn: 1545-5955
  databaseCode: RIE
  dateStart: 20040101
  isFulltext: true
  titleUrlDefault: https://ieeexplore.ieee.org/
  providerName: IEEE
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEB5s8aAHnxXrixw8Cam7m-wjx2JbRLQIVultyWtFKF2pW3-_k-yq9aDgLbtMYPmyyXyZZOYDOM_CJMWJFFERG0m5SQQVTITU-XZu0EMJX47h6TYdj7PpVNw3yeo-F8Za6y-f2Z5r-rN8U-qlC5XhDE-CFBlNC1ppmtTJWt-F9TIfUHGUgMYijpsjzDAQl5P-wxC3ghHvMY5LtuA_nNCKqop3KqPtf37ODmw17JH06-HehTU734PNlZqC-3AzkJWkg4VbxcjQZ_YRJ3jm0s5JWZA7f3vSPbjALpI_Mqmlekh_WZXIXyVpaq4-d-BxNJxcXdNGMIFqFoiKyizRRibo8lWBPA2ZnFGMF1pyhXNTs0KpMM7SItRWBYVENiCjKE2kigwLFJofQHtezu0hEIudhFO4C1nMLbZjEzKpkCwylSmbdSH4RDDXTTVxJ2oxy_2uIhC5Az13oOcN6F24-OryWpfS-Mu44wBfMayxPvrl_TFsRE6Z1wdHTqBdLZb2FNb1e_Xytjjzf8oH1NS5GQ
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bS8MwFA46BfXB68R5zYNPQmbbpJc8DrcxdRuCVfZWcqsIssrs_P2epFXng4JvaTmB8qXJ-XKScz6EzhM_imEiBYSHWhCmI0445T6xvp1p8FDclWN4HMbjcTKZ8Ls6Wd3lwhhj3OUz07ZNd5avCzW3oTKY4ZEXA6NZRishY4FXpWt9l9ZLXEjFkgIS8jCsDzF9j1-mnfsebAYD1qYMFm3OfrihBV0V51b6W__8oG20WfNH3KkGfActmeku2lioKriHbrqiFKQ7s-sY7rncPmwlz2ziOS5yPHL3J-2DDe0C_cNpJdaDO_OyAAYrcF119amJHvq99GpAaskEoqjHSyKSSGkRgdOXOTA14HJaUpYrwSTMTkVzKf0wiXNfGenlAviACII4EjLQ1JNgvo8a02JqDhA20IlbjTufhsxAO9Q-FRLoIpWJNEkLeZ8IZqquJ25lLV4yt6_weGZBzyzoWQ16C118dXmtimn8Zdy0gC8YVlgf_vL-DK0N0tEwG16Pb4_QemB1el2o5Bg1ytncnKBV9V4-v81O3V_zAaPOvGA
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+Energy+Modeling+of+Machining+Centers+Through+Automata+Learning&rft.jtitle=IEEE+transactions+on+automation+science+and+engineering&rft.au=Lestingi%2C+Livia&rft.au=Frigerio%2C+Nicla&rft.au=Bersani%2C+Marcello+M.&rft.au=Matta%2C+Andrea&rft.date=2025&rft.pub=IEEE&rft.issn=1545-5955&rft.volume=22&rft.spage=5769&rft.epage=5780&rft_id=info:doi/10.1109%2FTASE.2024.3430394&rft.externalDocID=10607999
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1545-5955&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1545-5955&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1545-5955&client=summon