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Semi-supervised multi-label feature selection algorithm for online monitoring of laser metal deposition manufacturing quality

dc.rights.licenseopenen_US
dc.contributor.authorWU, Ziqian
dc.contributor.authorXU, Zhenying
dc.contributor.authorFAN, Wei
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
dc.contributor.authorPOULHAON, Fabien
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
dc.contributor.authorMICHAUD, Pierre
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
dc.contributor.authorJOYOT, Pierre
ORCID: 0000-0002-6608-7343
IDREF: 085496057
dc.date.accessioned2023-08-24T13:07:40Z
dc.date.available2023-08-24T13:07:40Z
dc.date.issued2023-09-01
dc.identifier.issn0263-2241en_US
dc.identifier.urioai:crossref.org:10.1016/j.measurement.2023.113301
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/183505
dc.description.abstractEnLaser metal deposition (LMD) manufacturing, as a technique of laser metal additive manufacturing, has the advantages of short production periods, good economy and unrestricted forming shapes in the direct manufacturing of metal parts. However, the poor accuracy of single-object monitoring and the lack of labelled data make quality monitoring a huge challenge. Accordingly, this study extracts multi-features from the multi-object, including melt pool dimensions, spatters and temperature field, and proposes a semi-supervised multi-label feature selection (SSMLFS) algorithm. For SSMLFS algorithm, a multiple regression quality model is firstly proposed to produce different quality levels for unlabelled data based on microstructure. Then, to achieve feature dimension reduction and filter out highly relevant features, a quality correlation evaluation function is developed to calculate the contribution and ranking of the various features. Moreover, a local search algorithm based on quality is designed to improve the search speed of feature subsets and speed up the convergence of the SSMLFS algorithm. Experimental validation is conducted on BeAM Magic 800 machine and uses several commonly monitoring algorithms based on machine learning. Related experimental results prove that SSMLFS can significantly improve the accuracy and efficiency of quality monitoring compared to single-feature and full-feature monitoring. The proposed SSMLFS algorithm provides a semi-supervised feature selection framework to handle the low accuracy of single-feature monitoring and the complexity of multi-feature monitoring in the quality monitoring of LMD manufacturing.
dc.language.isoENen_US
dc.sourcecrossref
dc.title.enSemi-supervised multi-label feature selection algorithm for online monitoring of laser metal deposition manufacturing quality
dc.typeArticle de revueen_US
dc.identifier.doi10.1016/j.measurement.2023.113301en_US
dc.subject.halSciences de l'ingénieur [physics]en_US
bordeaux.journalMeasurement - Journal of the International Measurement Confederation (IMEKO)en_US
bordeaux.page113301en_US
bordeaux.volume219en_US
bordeaux.hal.laboratoriesESTIA - Rechercheen_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionBordeaux INPen_US
bordeaux.institutionBordeaux Sciences Agroen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcedissemin
hal.identifierhal-04187252
hal.version1
hal.date.transferred2023-08-24T13:07:53Z
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exporttrue
workflow.import.sourcedissemin
dc.rights.ccPas de Licence CCen_US
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