Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotors

This paper aims to provide researchers and engineers with evidence that sensorless machine variable monitoring can achieve tool wear monitoring in broaching in real production environments, reducing production errors, enhancing product quality, and facilitating zero-defect manufacturing. Additionall...

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Autores: Aldekoa Gallarza,Iñigo, Del Olmo Sanz, Ander, Sastoque Pinilla, Edwar Leonardo, Sendino Mouliet, Sara, López Novoa, Unai, López de Lacalle Marcaide, Luis Norberto
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/63669
Acceso en línea:http://hdl.handle.net/10810/63669
Access Level:acceso abierto
Palabra clave:broaching process
process monitoring
tool wear estimation
sensorless approach
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spelling Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotorsAldekoa Gallarza,IñigoDel Olmo Sanz, AnderSastoque Pinilla, Edwar LeonardoSendino Mouliet, SaraLópez Novoa, UnaiLópez de Lacalle Marcaide, Luis Norbertobroaching processprocess monitoringtool wear estimationsensorless approachThis paper aims to provide researchers and engineers with evidence that sensorless machine variable monitoring can achieve tool wear monitoring in broaching in real production environments, reducing production errors, enhancing product quality, and facilitating zero-defect manufacturing. Additionally, broaching plays a crucial role in improving the quality of manufacturing products and processes. These aspects are especially pertinent in aeronautical manufacturing, which serves as the experimental case in this study. The research presents findings that establish a correlation between the variables of a broaching machine’s servomotors and the condition of the broaching tools. The authors propose an effective method for measuring broaching tool wear without external sensors and provide a detailed explanation of the methodology, enabling reproducibility of similar results. The results stem from three trials conducted on an electromechanical vertical broaching machine, utilizing cemented carbide grade broaching tools to broach a superalloy Inconel 718 test piece. The machine data collected facilitated the training of a set of machine learning models, accurately estimating tool wear on the broaches. Each model demonstrates high predictive accuracy, with a coefficient of determination surpassing 0.9.Thanks are addressed to MCIN/AEI/10.13039/501100011033/and European Union NextGenerationEU/ PRTR” - Proyectos de Transición Ecológica y Transición Digital , Quolink: A new way to assess quality in manufacturing processes by merging process data in high connected production systems in aeroturbines, Ref TED2021-130044B-I00. Thanks are also addressed to Basque, Spain for the support of University research groups, grant IT1573-22. Thanks are also due to European commission by H2020 project n. 958357, and it is an initiative of the Factories-of-the-Future (FoF) Public Private Partnership, project InterQ Interlinked Process, Product and Data Quality Framework for Zero-Defects Manufacturing. Results were analyzed by models developed in Project KK-2022/0065 Lanverso and Hatasu. This work was also partially supported by the Spanish Ministerio de Asuntos Económicos y Transformación Digital and the European Union NextGenerationEU through the project LocoForge: Mimbres instantiation for railways and Industry 5.0 vertical sectors (grant TSI-063000- 2021-47), funded by the Plan for Recovery, Transformation and Resilience .ElsevierEuropean Commission202320232023info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/63669reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/MICINN/TED2021-130044B-I00/info:eu-repo/grantAgreement/EC/H2020/958357https://www.sciencedirect.com/science/article/pii/S0888327023006817info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/3.0/es/© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Atribución-NoComercial-SinDerivadas 3.0 Españaoai:addi.ehu.eus:10810/636692026-06-18T09:23:17Z
dc.title.none.fl_str_mv Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotors
title Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotors
spellingShingle Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotors
Aldekoa Gallarza,Iñigo
broaching process
process monitoring
tool wear estimation
sensorless approach
title_short Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotors
title_full Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotors
title_fullStr Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotors
title_full_unstemmed Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotors
title_sort Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotors
dc.creator.none.fl_str_mv Aldekoa Gallarza,Iñigo
Del Olmo Sanz, Ander
Sastoque Pinilla, Edwar Leonardo
Sendino Mouliet, Sara
López Novoa, Unai
López de Lacalle Marcaide, Luis Norberto
author Aldekoa Gallarza,Iñigo
author_facet Aldekoa Gallarza,Iñigo
Del Olmo Sanz, Ander
Sastoque Pinilla, Edwar Leonardo
Sendino Mouliet, Sara
López Novoa, Unai
López de Lacalle Marcaide, Luis Norberto
author_role author
author2 Del Olmo Sanz, Ander
Sastoque Pinilla, Edwar Leonardo
Sendino Mouliet, Sara
López Novoa, Unai
López de Lacalle Marcaide, Luis Norberto
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv European Commission
dc.subject.none.fl_str_mv broaching process
process monitoring
tool wear estimation
sensorless approach
topic broaching process
process monitoring
tool wear estimation
sensorless approach
description This paper aims to provide researchers and engineers with evidence that sensorless machine variable monitoring can achieve tool wear monitoring in broaching in real production environments, reducing production errors, enhancing product quality, and facilitating zero-defect manufacturing. Additionally, broaching plays a crucial role in improving the quality of manufacturing products and processes. These aspects are especially pertinent in aeronautical manufacturing, which serves as the experimental case in this study. The research presents findings that establish a correlation between the variables of a broaching machine’s servomotors and the condition of the broaching tools. The authors propose an effective method for measuring broaching tool wear without external sensors and provide a detailed explanation of the methodology, enabling reproducibility of similar results. The results stem from three trials conducted on an electromechanical vertical broaching machine, utilizing cemented carbide grade broaching tools to broach a superalloy Inconel 718 test piece. The machine data collected facilitated the training of a set of machine learning models, accurately estimating tool wear on the broaches. Each model demonstrates high predictive accuracy, with a coefficient of determination surpassing 0.9.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/63669
url http://hdl.handle.net/10810/63669
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MICINN/TED2021-130044B-I00/
info:eu-repo/grantAgreement/EC/H2020/958357
https://www.sciencedirect.com/science/article/pii/S0888327023006817
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
Atribución-NoComercial-SinDerivadas 3.0 España
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/3.0/es/
Atribución-NoComercial-SinDerivadas 3.0 España
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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