Optimal window size for the extraction of features for tool wear estimation

Descripción del Articulo

This work was funded by CONCYTEC-FONDECYT in the framework of call E038-01, grant No. 020-2019-FONDECYT-BM-INC.INV.
Detalles Bibliográficos
Autores: Casusol A.J., Zegarra F.C., Vargas-Machuca J., Coronado A.M.
Formato: objeto de conferencia
Fecha de Publicación:2021
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/3031
Enlace del recurso:https://hdl.handle.net/20.500.12390/3031
https://doi.org/10.1109/INTERCON52678.2021.9532759
Nivel de acceso:acceso abierto
Materia:SVR
CNC milling machine
feature engineering
hyperparameter optimization
https://purl.org/pe-repo/ocde/ford#2.03.01
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oai_identifier_str oai:repositorio.concytec.gob.pe:20.500.12390/3031
network_acronym_str CONC
network_name_str CONCYTEC-Institucional
repository_id_str 4689
dc.title.none.fl_str_mv Optimal window size for the extraction of features for tool wear estimation
title Optimal window size for the extraction of features for tool wear estimation
spellingShingle Optimal window size for the extraction of features for tool wear estimation
Casusol A.J.
SVR
CNC milling machine
feature engineering
hyperparameter optimization
https://purl.org/pe-repo/ocde/ford#2.03.01
title_short Optimal window size for the extraction of features for tool wear estimation
title_full Optimal window size for the extraction of features for tool wear estimation
title_fullStr Optimal window size for the extraction of features for tool wear estimation
title_full_unstemmed Optimal window size for the extraction of features for tool wear estimation
title_sort Optimal window size for the extraction of features for tool wear estimation
author Casusol A.J.
author_facet Casusol A.J.
Zegarra F.C.
Vargas-Machuca J.
Coronado A.M.
author_role author
author2 Zegarra F.C.
Vargas-Machuca J.
Coronado A.M.
author2_role author
author
author
dc.contributor.author.fl_str_mv Casusol A.J.
Zegarra F.C.
Vargas-Machuca J.
Coronado A.M.
dc.subject.none.fl_str_mv SVR
topic SVR
CNC milling machine
feature engineering
hyperparameter optimization
https://purl.org/pe-repo/ocde/ford#2.03.01
dc.subject.es_PE.fl_str_mv CNC milling machine
feature engineering
hyperparameter optimization
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.03.01
description This work was funded by CONCYTEC-FONDECYT in the framework of call E038-01, grant No. 020-2019-FONDECYT-BM-INC.INV.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.available.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.issued.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/3031
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1109/INTERCON52678.2021.9532759
dc.identifier.scopus.none.fl_str_mv 2-s2.0-85116228625
url https://hdl.handle.net/20.500.12390/3031
https://doi.org/10.1109/INTERCON52678.2021.9532759
identifier_str_mv 2-s2.0-85116228625
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv Proceedings of the 2021 IEEE 28th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2021
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
dc.source.none.fl_str_mv reponame:CONCYTEC-Institucional
instname:Consejo Nacional de Ciencia Tecnología e Innovación
instacron:CONCYTEC
instname_str Consejo Nacional de Ciencia Tecnología e Innovación
instacron_str CONCYTEC
institution CONCYTEC
reponame_str CONCYTEC-Institucional
collection CONCYTEC-Institucional
repository.name.fl_str_mv Repositorio Institucional CONCYTEC
repository.mail.fl_str_mv repositorio@concytec.gob.pe
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spelling Publicationrp08678600rp08675600rp08677600rp08676600Casusol A.J.Zegarra F.C.Vargas-Machuca J.Coronado A.M.2024-05-30T23:13:38Z2024-05-30T23:13:38Z2021https://hdl.handle.net/20.500.12390/3031https://doi.org/10.1109/INTERCON52678.2021.95327592-s2.0-85116228625This work was funded by CONCYTEC-FONDECYT in the framework of call E038-01, grant No. 020-2019-FONDECYT-BM-INC.INV.Prediction of machine tool wear is highly dependent on the quality of the measured data and the ability to extract information from such raw data. These data are presented in the form of time series, which cannot be used directly by conventional machine learning algorithms, such as the one used in this work. To link the raw data and the learning algorithm, it is first necessary to extract a feature set from the time series. An important but little analyzed aspect is the size of the window required for feature extraction. If this window is too small, not much information will be obtained, on the other hand, if the window is too large, there will be more chance of outliers and other irregularities of the data being introduced. In the present work, we use a novel database corresponding to machine tool wear to demonstrate the impact of window size. An optimally chosen window size, plus an adequate feature extraction, allows us to obtain results comparable to the state of the art, i.e., median scores of 89 %, which are comparable to that obtained by the first place of the recently held data challenge. © 2021 IEEE.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengInstitute of Electrical and Electronics Engineers Inc.Proceedings of the 2021 IEEE 28th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2021info:eu-repo/semantics/openAccessSVRCNC milling machine-1feature engineering-1hyperparameter optimization-1https://purl.org/pe-repo/ocde/ford#2.03.01-1Optimal window size for the extraction of features for tool wear estimationinfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC20.500.12390/3031oai:repositorio.concytec.gob.pe:20.500.12390/30312024-05-30 16:13:18.72http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="824f0cce-6a5e-46d6-b86a-748a71374695"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Optimal window size for the extraction of features for tool wear estimation</Title> <PublishedIn> <Publication> <Title>Proceedings of the 2021 IEEE 28th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2021</Title> </Publication> </PublishedIn> <PublicationDate>2021</PublicationDate> <DOI>https://doi.org/10.1109/INTERCON52678.2021.9532759</DOI> <SCP-Number>2-s2.0-85116228625</SCP-Number> <Authors> <Author> <DisplayName>Casusol A.J.</DisplayName> <Person id="rp08678" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Zegarra F.C.</DisplayName> <Person id="rp08675" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Vargas-Machuca J.</DisplayName> <Person id="rp08677" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Coronado A.M.</DisplayName> <Person id="rp08676" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Institute of Electrical and Electronics Engineers Inc.</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>SVR</Keyword> <Keyword>CNC milling machine</Keyword> <Keyword>feature engineering</Keyword> <Keyword>hyperparameter optimization</Keyword> <Abstract>Prediction of machine tool wear is highly dependent on the quality of the measured data and the ability to extract information from such raw data. These data are presented in the form of time series, which cannot be used directly by conventional machine learning algorithms, such as the one used in this work. To link the raw data and the learning algorithm, it is first necessary to extract a feature set from the time series. An important but little analyzed aspect is the size of the window required for feature extraction. If this window is too small, not much information will be obtained, on the other hand, if the window is too large, there will be more chance of outliers and other irregularities of the data being introduced. In the present work, we use a novel database corresponding to machine tool wear to demonstrate the impact of window size. An optimally chosen window size, plus an adequate feature extraction, allows us to obtain results comparable to the state of the art, i.e., median scores of 89 %, which are comparable to that obtained by the first place of the recently held data challenge. © 2021 IEEE.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1
score 13.394457
Optimal window size for the extraction of features for tool wear estimation
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La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).

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