• Treffer 5 von 5
Zurück zur Trefferliste

Potentials and challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring in laser powder bed fusion

  • Laser powder bed fusion is one of the most promising additive manufacturing techniques for printing complex-shaped metal components. However, the formation of subsurface porosity poses a significant risk to the service lifetime of the printed parts. In-situ monitoring offers the possibility to detect porosity already during manufacturing. Thereby, process feedback control or a manual process interruption to cut financial losses is enabled. Short-wave infrared thermography can monitor the thermal history of manufactured parts which is closely connected to the probability of porosity formation. Artificial intelligence methods are increasingly used for porosity prediction from the obtained large amounts of complex monitoring data. In this study, we aim to identify the potential and the challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring. Therefore, the porosity prediction task is studied in detail using an exemplary dataset from theLaser powder bed fusion is one of the most promising additive manufacturing techniques for printing complex-shaped metal components. However, the formation of subsurface porosity poses a significant risk to the service lifetime of the printed parts. In-situ monitoring offers the possibility to detect porosity already during manufacturing. Thereby, process feedback control or a manual process interruption to cut financial losses is enabled. Short-wave infrared thermography can monitor the thermal history of manufactured parts which is closely connected to the probability of porosity formation. Artificial intelligence methods are increasingly used for porosity prediction from the obtained large amounts of complex monitoring data. In this study, we aim to identify the potential and the challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring. Therefore, the porosity prediction task is studied in detail using an exemplary dataset from the manufacturing of two Haynes282 cuboid components. Our trained 1D convolutional neural network model shows high performance (R² score of 0.90) for the prediction of local porosity in discrete sub-volumes with dimensions of (700 x 700 x 40) μm³. It could be demonstrated that the regressor correctly predicts layer-wise porosity changes but presumably has limited capability to predict differences in local porosity. Furthermore, there is a need to study the significance of the used thermogram feature inputs to streamline the model and to adjust the monitoring hardware. Moreover, we identified multiple sources of data uncertainty resulting from the in-situ monitoring setup, the registration with the ground truth X-ray-computed tomography data and the used pre-processing workflow that might influence the model’s performance detrimentally.zeige mehrzeige weniger

Volltext Dateien herunterladen

  • TEME_corr_Oster_Potentials_and_Challenges_of_deep_learning_assisted_porosity_detection.pdf
    eng

    Zu dieser Veröffentlichung existiert ein Corrigendum, ein Link befindet sich im Feld "Zugehöriger Identifikator" - There is a corrigendum for this publication, a link is given in the field "Related identifier"

Metadaten exportieren

Weitere Dienste

Teilen auf Twitter Suche bei Google Scholar Anzahl der Zugriffe auf dieses Dokument
Metadaten
Autor*innen:Simon OsterORCiD, Nils ScheuschnerORCiD, Keerthana Chand, Simon AltenburgORCiD, G. GerlachORCiD
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Deutsch):Technisches Messen
Jahr der Erstveröffentlichung:2023
Organisationseinheit der BAM:8 Zerstörungsfreie Prüfung
8 Zerstörungsfreie Prüfung / 8.0 Abteilungsleitung und andere
8 Zerstörungsfreie Prüfung / 8.5 Röntgenbildgebung
Verlag:De Gruyter
Verlagsort:Berlin
Jahrgang/Band:90
Erste Seite:85
Letzte Seite:96
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Angewandte Physik
Freie Schlagwörter:Defect detection; Laser powder bed fusion (PBF-LB/M, L-PBF); Machine learning; Porosity prediction; Selective laser melting; Thermography
Themenfelder/Aktivitätsfelder der BAM:Chemie und Prozesstechnik
Material
Material / Additive Fertigung
Veranstaltung:XXXVII. Messtechnisches Symposium 2023
Veranstaltungsort:Freiburg, Germany
Beginndatum der Veranstaltung:27.09.2023
Enddatum der Veranstaltung:28.09.2023
DOI:10.1515/teme-2023-0062
ISSN:0171-8096
ISSN:2196-7113
Zugehöriger Identifikator:https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/59471
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:29.09.2023
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:04.10.2023
Paper des Monats:Ja
Einverstanden
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.