YADAV, Pinku; SINGH, Vibhutesh Kumar; JOFFRE, Thomas; RIGO, Olivier; ARVIEU, Corinne; LE GUEN, Emilie; LACOSTE, Eric

doi:10.1002/adem.202000660

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YADAV, Pinku
Institut de Mécanique et d'Ingénierie [I2M]

SINGH, Vibhutesh Kumar
University College Dublin [Dublin] [UCD]

JOFFRE, Thomas
Centre Technique Industriel de la Plasturgie et des Composites [IPC]

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Advanced Engineering Materials. 2020-12, vol. 22, n° 12

Wiley-VCH Verlag

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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.< Réduire

Mots clés en anglais

direct metal laser sintering

in situ monitoring

machine learning

optical tomography

quality assurance

semisupervised learning

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

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