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Inline drift detection using monitoring systems and machine learning in selective laser melting
hal.structure.identifier | Institut de Mécanique et d'Ingénierie [I2M] | |
dc.contributor.author | YADAV, Pinku | |
hal.structure.identifier | University College Dublin [Dublin] [UCD] | |
dc.contributor.author | SINGH, Vibhutesh Kumar | |
hal.structure.identifier | Centre Technique Industriel de la Plasturgie et des Composites [IPC] | |
dc.contributor.author | JOFFRE, Thomas | |
hal.structure.identifier | Sirris - Software Engineering & ICT Group | |
dc.contributor.author | RIGO, Olivier | |
hal.structure.identifier | Institut de Mécanique et d'Ingénierie [I2M] | |
dc.contributor.author | ARVIEU, Corinne | |
hal.structure.identifier | Institut de Mécanique et d'Ingénierie [I2M] | |
dc.contributor.author | LE GUEN, Emilie | |
hal.structure.identifier | Institut de Mécanique et d'Ingénierie [I2M] | |
dc.contributor.author | LACOSTE, Eric | |
dc.date.accessioned | 2021-05-14T09:30:29Z | |
dc.date.available | 2021-05-14T09:30:29Z | |
dc.date.issued | 2020-12 | |
dc.identifier.issn | 1438-1656 | |
dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/75816 | |
dc.description.abstractEn | Direct 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.iso | en | |
dc.publisher | Wiley-VCH Verlag | |
dc.subject.en | direct metal laser sintering | |
dc.subject.en | in situ monitoring | |
dc.subject.en | machine learning | |
dc.subject.en | optical tomography | |
dc.subject.en | quality assurance | |
dc.subject.en | semisupervised learning | |
dc.title.en | Inline drift detection using monitoring systems and machine learning in selective laser melting | |
dc.type | Article de revue | |
dc.identifier.doi | 10.1002/adem.202000660 | |
dc.subject.hal | Sciences de l'ingénieur [physics] | |
bordeaux.journal | Advanced Engineering Materials | |
bordeaux.volume | 22 | |
bordeaux.hal.laboratories | Institut de Mécanique et d’Ingénierie de Bordeaux (I2M) - UMR 5295 | * |
bordeaux.issue | 12 | |
bordeaux.institution | Université de Bordeaux | |
bordeaux.institution | Bordeaux INP | |
bordeaux.institution | CNRS | |
bordeaux.institution | INRAE | |
bordeaux.institution | Arts et Métiers | |
bordeaux.peerReviewed | oui | |
hal.identifier | hal-03167078 | |
hal.version | 1 | |
hal.origin.link | https://hal.archives-ouvertes.fr//hal-03167078v1 | |
bordeaux.COinS | ctx_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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