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Inline drift detection using monitoring systems and machine learning in selective laser melting

hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorYADAV, Pinku
hal.structure.identifierUniversity College Dublin [Dublin] [UCD]
dc.contributor.authorSINGH, Vibhutesh Kumar
hal.structure.identifierCentre Technique Industriel de la Plasturgie et des Composites [IPC]
dc.contributor.authorJOFFRE, Thomas
hal.structure.identifierSirris - Software Engineering & ICT Group
dc.contributor.authorRIGO, Olivier
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorARVIEU, Corinne
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorLE GUEN, Emilie
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorLACOSTE, Eric
dc.date.accessioned2021-05-14T09:30:29Z
dc.date.available2021-05-14T09:30:29Z
dc.date.issued2020-12
dc.identifier.issn1438-1656
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/75816
dc.description.abstractEnDirect metal laser sintering, an additive manufacturing technique, has a huge growing demand in industries like aerospace, biomedical, and tooling sector due to its capability to manufacture complex parts with ease. Despite many technological advancements, the reliability and repeatability of the process are still an issue. Therefore, there is a demand for inline automatic fault detection and postprocessing tools to analyze the acquired in situ monitoring data aiming to provide better-quality assurance to the user. Herein, the treatment of the data obtained using the EOSTATE optical tomography monitoring system is focused. A balanced dataset is obtained with the help of computer tomography of the certified part (Stainless Steel CX cylindrical samples), through which a feature matrix is prepared, and the layers of the part are classified either having "Drift" or "No-drift." The model is trained with the feature matrix and tested on benchmark parts (Maraging Steel) and on an industrial part (knuckle, automotive part) manufactured in AlSi10Mg. The proposed semisupervised approach shows promising results for presented case studies. Thus, the semisupervised machine learning approach, if adopted, could prove to be a cost effective and fast approach to postprocess the in situ monitoring data with much ease.
dc.language.isoen
dc.publisherWiley-VCH Verlag
dc.subject.endirect metal laser sintering
dc.subject.enin situ monitoring
dc.subject.enmachine learning
dc.subject.enoptical tomography
dc.subject.enquality assurance
dc.subject.ensemisupervised learning
dc.title.enInline drift detection using monitoring systems and machine learning in selective laser melting
dc.typeArticle de revue
dc.identifier.doi10.1002/adem.202000660
dc.subject.halSciences de l'ingénieur [physics]
bordeaux.journalAdvanced Engineering Materials
bordeaux.volume22
bordeaux.hal.laboratoriesInstitut de Mécanique et d’Ingénierie de Bordeaux (I2M) - UMR 5295*
bordeaux.issue12
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.institutionINRAE
bordeaux.institutionArts et Métiers
bordeaux.peerReviewedoui
hal.identifierhal-03167078
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03167078v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Advanced%20Engineering%20Materials&rft.date=2020-12&rft.volume=22&rft.issue=12&rft.eissn=1438-1656&rft.issn=1438-1656&rft.au=YADAV,%20Pinku&SINGH,%20Vibhutesh%20Kumar&JOFFRE,%20Thomas&RIGO,%20Olivier&ARVIEU,%20Corinne&rft.genre=article


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