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Machine learning approach in non-intrusive monitoring of tool wear evolution in massive CFRP automatic drilling processes in the aircraft industry

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dc.affiliation.areaUC3M. Área de Ingeniería Mecánicaes
dc.affiliation.dptoUC3M. Departamento de Ingeniería Mecánicaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Tecnologías de Fabricación y Diseño de Componentes Mecánicos y Biomecánicoses
dc.contributor.authorDomínguez-Monferrer, C.
dc.contributor.authorFernández-Pérez, J.
dc.contributor.authorSantos, Raul de
dc.contributor.authorMiguélez Garrido, María Henar
dc.contributor.authorCantero Guisández, José Luis
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.contributor.funderMinisterio de Educación, Cultura y Deporte (España)es
dc.date.accessioned2023-04-21T13:59:57Z
dc.date.available2023-04-21T13:59:57Z
dc.date.issued2022-10
dc.description.abstractThis research presents an analysis of real production data of an automatic drilling industrial system and emphasizes its ability as a process control indicator in terms of tool wear. In particular, the study is framed in Carbon-fiber-reinforced polymer composites (CFRPs) drilling operations carried out at Airbus facilities. The industrial process data were directly collected from the manufacturing plant in Getafe (in the Madrid-Spain region) and come from three different sources: spindle power consumption signals, obtained from the internal instrumentation of the machine, cutting tools wear analysis, and hole quality inspection. The main goal is to use different machining features such as tool accumulated cutting time, together with signal features to feed Machine Learning (ML) algorithms to predict tool wear. To address the inherent variability of complex production systems, it has been proposed a specific methodology that is applicable to control machining operations. The approach includes data collection, data pre-processing, and the application of Linear Regression, k-Nearest Neighbors, and Random Forest ML algorithms. As an outcome to be predicted, a novel qualitative scale of the general condition of the drill is proposed. The predictive models show promising results bearing in mind the quality and quantity of the available data – up to 3500 holes drilled with 8 diamond-coated tungsten carbide tools under different work conditions (number of layers, thickness, and others). The relevance of the benchmarks defined as representative features of the spindle power consumption as well as other machining-related parameters and their relationship with tool wear has been discussed. The Random Forest model gets the best results, being the most interesting variables the accumulated cutting time and the maximum spindle power consumption, and the most irrelevant, the number of parts to be drilled.en
dc.description.sponsorshipThe authors acknowledge the financial support to AIRBUS S.A.S through the project CFT - AI - PJMT - DRILLING PROCESS IMPROVEMENT BASED ON DATA ANALYTICS, to the State Investigation Agency through the project ANALYSIS OF DEFECTS IN FIBER-REINFORCED LAMINATES DUE TO MANUFACTURING PROCESSES AND EFFECT ON FATIGUE BEHAVIOR (PID2020-118480RB-C22) and the project DIGITALIZATION OF INDUSTRIAL DRILLING PROCESS (PDC2021-121368-C21), the Regional Ministry of Education, Youth and Sports of the CAM and the European Social Fund for funding the Aid for the Hiring of a Research Assistant (PEJ-2020-AI/IND-18025) and the MCIN/AEI/10.13039/501100011033 and the European Union "NextGenerationEU"/PRTR" (PDC2021-121368-C21).en
dc.format.extent18
dc.identifier.bibliographicCitationDomínguez-Monferrer, C., Fernández-Pérez, J., De Santos, R., Miguélez, M., & Cantero, J. (2022). Machine learning approach in non-intrusive monitoring of tool wear evolution in massive CFRP automatic drilling processes in the aircraft industry. Journal of Manufacturing Systems, 65, 622-639.en
dc.identifier.doihttps://doi.org/10.1016/j.jmsy.2022.10.018
dc.identifier.issn0278-6125
dc.identifier.publicationfirstpage622
dc.identifier.publicationlastpage639
dc.identifier.publicationtitleJournal of Manufacturing Systemsen
dc.identifier.publicationvolume65
dc.identifier.urihttps://hdl.handle.net/10016/37172
dc.identifier.uxxiAR/0000031988
dc.language.isoeng
dc.publisherElsevieren
dc.relation.projectIDComunidad de Madrid. PEJ-2020-AI/IND-18025es
dc.relation.projectIDGobierno de España. PID2020-118480RB-C22es
dc.relation.projectIDGobierno de España. PDC2021-121368-C21es
dc.relation.projectIDAT-2022
dc.rights© 2022 The Authors.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaAeronáuticaes
dc.subject.ecienciaBiología y Biomedicinaes
dc.subject.ecienciaIngeniería Industriales
dc.subject.ecienciaIngeniería Mecánicaes
dc.subject.ecienciaMaterialeses
dc.subject.otherDigitizationen
dc.subject.otherMachine learningen
dc.subject.otherPredictive maintenanceen
dc.subject.otherSpindle power consumptionen
dc.subject.otherTool wear monitoringen
dc.titleMachine learning approach in non-intrusive monitoring of tool wear evolution in massive CFRP automatic drilling processes in the aircraft industryen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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