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CFRP drilling process control based on spindle power consumption from real production data 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, Carlos
dc.contributor.authorFernández Pérez, Juan
dc.contributor.authorSantos Garcia, Raul De
dc.contributor.authorMiguélez Garrido, María Henar
dc.contributor.authorCantero Guisández, José Luis
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Educación, Cultura y Deporte (España)es
dc.date.accessioned2023-03-06T12:31:53Z
dc.date.available2023-03-06T12:31:53Z
dc.date.issued2022
dc.descriptionProceedings of: 55th CIRP Conference on Manufacturing Systems (CMS 2022), 29 June-01 July 2022, Lugano, Switzerland.en
dc.description.abstractOngoing challenges in advanced manufacturing highlight the need of improved control of the production system, higher production speed, lower tool wear, top quality standards together with reduced material waste to minimize associated costs, time and environmental footprint. The organization of production resources through the integration of data along the value chain using Information Technologies is required to achieve these challenges. Thus, the emergence of the fourth industry revolution brings with it the organization of productive resources through computational intelligence and connectivity. This research seeks to analyze the spindle power consumption in Carbon-fiber-reinforced polymer composites (CFRPs) drilling operations as a process control indicator in terms of tool wear. This signal stands out among others available because it can be obtained in real time, with high quality and through a non-intrusive methodology. In particular, the study is framed in the real production system in factories of Airbus. The industrial process data were directly collected from the manufacturing plant in Getafe (in the Madrid-Spain region) and correspond to more than 3000 holes drilled with diamond-coated tungsten carbide tools. The variability of machining conditions in the aeronautical component drilling process and inherent noise level of signals obtained in industrial environments required the development of a data wrangling methodology to structure and clean the information. As a result, different magnitudes were obtained from spindle power consumption signal related to tool wear with low levels of dependence on drilling conditions. The conclusions of this work are directly applicable to the control of industrial production systems within the framework of Industry 4.0, searching new improvement opportunities through Data analytics and Artificial Intelligence such as tool breakage detection or the optimization of cycle times.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 to 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).en
dc.format.extent6
dc.identifier.bibliographicCitationDomínguez-Monferrer, C., Fernández-Pérez, J., de Santos, R., Miguélez, M. H., & Cantero, J.L. (29 June-01 July 2022). CFRP drilling process control based on spindle power consumption from real production data in the aircraft industry [proceedings]. 55th CIRP Conference on Manufacturing Systems (CMS 2022), Lugano, Switzerland. Published in Procedia CIRP, 107, 2022, 1533-1538.en
dc.identifier.doihttps://doi.org/10.1016/j.procir.2022.05.187
dc.identifier.isbn2212-8271
dc.identifier.publicationfirstpage1533
dc.identifier.publicationlastpage1538
dc.identifier.publicationtitleProcedia CIRP: Leading manufacturing systems transformation - Proceedings of the 55th CIRP Conference on Manufacturing Systems 2022en
dc.identifier.publicationvolume107
dc.identifier.urihttps://hdl.handle.net/10016/36761
dc.identifier.uxxiCC/0000034106
dc.language.isoeng
dc.publisherElsevieren
dc.relation.eventdate2022-06-29
dc.relation.eventplaceSuizaes
dc.relation.eventtitleCMS 2022: 55th CIRP Conference on Manufacturing Systemsen
dc.relation.projectIDComunidad de Madrid. PEJ-2020-AI/IND-18025es
dc.relation.projectIDGobierno de España. PID2020-118480RB-C22es
dc.rights© 2022 The Author(s).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.ecienciaIngeniería Mecánicaes
dc.subject.otherData analysisen
dc.subject.otherAdvanced manufacturing controlen
dc.subject.otherDigitizationen
dc.subject.otherIndustry 4.0en
dc.titleCFRP drilling process control based on spindle power consumption from real production data in the aircraft industryen
dc.typeconference paper*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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