A statistical data-based approach to instability detection and wear prediction in radial turning processes

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dc.contributor.authorJimenez Cortadi, Alberto
dc.contributor.authorIrigoien, Itziar
dc.contributor.authorBoto, Fernando
dc.contributor.authorSierra, Basilio
dc.contributor.authorSuarez, Alfredo
dc.contributor.authorGalar, Diego
dc.contributor.institutionTecnalia Research & Innovation
dc.contributor.institutionFACTORY
dc.contributor.institutionFABRIC_INTEL
dc.date.issued2018
dc.descriptionPublisher Copyright: © 2018, Polish Academy of Sciences Branch Lublin. All rights reserved.
dc.description.abstractRadial turning forces for tool-life improvements are studied, with the emphasis on predictive rather than preventive maintenance. A tool for wear prediction in various experimental settings of instability is proposed through the application of two statistical approaches to process data on tool-wear during turning processes: three sigma edit rule analysis and Principal Component Analysis (PCA). A Linear Mixed Model (LMM) is applied for wear prediction. These statistical approaches to instability detection generate results of acceptable accuracy for delivering expert opinion. They may be used for on-line monitoring to improve the processing of different materials. The LMM predicted significant differences for tool wear when turning different alloys and with different lubrication systems. It also predicted the degree to which the turning process could be extended while conserving stability. Finally, it should be mentioned that tool force in contact with the material was not considered to be an important input variable for the model.en
dc.description.statusPeer reviewed
dc.format.extent8
dc.format.extent1235842
dc.identifier.citationJimenez Cortadi , A , Irigoien , I , Boto , F , Sierra , B , Suarez , A & Galar , D 2018 , ' A statistical data-based approach to instability detection and wear prediction in radial turning processes ' , Eksploatacja i Niezawodnosc - Maintenance and Reliability , vol. 20 , no. 3 , pp. 405-412 . https://doi.org/10.17531/ein.2018.3.8
dc.identifier.doi10.17531/ein.2018.3.8
dc.identifier.otherresearchoutputwizard: 11556/585
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85048594306&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofEksploatacja i Niezawodnosc - Maintenance and Reliability
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsRadial turning
dc.subject.keywordsTool-life improvement
dc.subject.keywordsInstability detection
dc.subject.keywordsWear prediction
dc.subject.keywordsLinear Mixed Models
dc.subject.keywordsRadial turning
dc.subject.keywordsTool-life improvement
dc.subject.keywordsInstability detection
dc.subject.keywordsWear prediction
dc.subject.keywordsLinear Mixed Models
dc.subject.keywordsSafety, Risk, Reliability and Quality
dc.subject.keywordsIndustrial and Manufacturing Engineering
dc.subject.keywordsProject ID
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/FP7/620134/EU/High speed metallic material removal under acceptable surface integrity for rotating frame/HIMMOVAL
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/FP7/620134/EU/High speed metallic material removal under acceptable surface integrity for rotating frame/HIMMOVAL
dc.subject.keywordsFunding Info
dc.subject.keywordsThe work was performed as a part of the HIMMOVAL (Grant Agreement Number: 620134) project within the CLEAN-SKY program, linked to the SAGE2 project for geared open-rotor development and the delivery of the demonstrator part. Funding through grant IT900-16 is also acknowledged from the Basque Government Department of Education, Universities and Research.
dc.subject.keywordsThe work was performed as a part of the HIMMOVAL (Grant Agreement Number: 620134) project within the CLEAN-SKY program, linked to the SAGE2 project for geared open-rotor development and the delivery of the demonstrator part. Funding through grant IT900-16 is also acknowledged from the Basque Government Department of Education, Universities and Research.
dc.titleA statistical data-based approach to instability detection and wear prediction in radial turning processesen
dc.typejournal article
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