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An integrated approach for detecting and classifying pores and surface topology for fatigue assessment 316L manufactured by powder bed fusion of metals using a laser beam using μ$$ \mu $$CT and machine learning algorithms

  • AbstractThis research aims to detect and analyze critical internal and surface defects in metal components manufactured by powder bed fusion of metals using a laser beam (PBF‐LB/M). The aim is to assess their impact on the fatigue behavior. Therefore, a combination of methods, including image processing of micro‐computed tomography (CT) scans, fatigue testing, and machine learning, was applied. A workflow was established to contribute to the nondestructive assessment of component quality and mechanical properties. Additionally, this study illustrates the application of machine learning to address a classification problem, specifically the categorization of pores into gas pores and lack of fusion pores. Although it was shown that internal defects exhibited a reduced impact on fatigue behavior compared with surface defects, it was noted that surface defects exert a higher influence on fatigue behavior. A machine learning algorithm was developed to predict the fatigue life using surfaceAbstractThis research aims to detect and analyze critical internal and surface defects in metal components manufactured by powder bed fusion of metals using a laser beam (PBF‐LB/M). The aim is to assess their impact on the fatigue behavior. Therefore, a combination of methods, including image processing of micro‐computed tomography (CT) scans, fatigue testing, and machine learning, was applied. A workflow was established to contribute to the nondestructive assessment of component quality and mechanical properties. Additionally, this study illustrates the application of machine learning to address a classification problem, specifically the categorization of pores into gas pores and lack of fusion pores. Although it was shown that internal defects exhibited a reduced impact on fatigue behavior compared with surface defects, it was noted that surface defects exert a higher influence on fatigue behavior. A machine learning algorithm was developed to predict the fatigue life using surface defect features as input parameters.zeige mehrzeige weniger

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Metadaten
Autor*innen:Johannes DillerORCiD, Ludwig Siebert, Michael WinklerORCiD, Dorina SiebertORCiD, Jakob Blankenhagen, David Wenzler, Christina Radlbeck, Martin MensingerORCiD
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Fatigue & Fracture of Engineering Materials & Structures
Jahr der Erstveröffentlichung:2024
Organisationseinheit der BAM:9 Komponentensicherheit
9 Komponentensicherheit / 9.3 Schweißtechnische Fertigungsverfahren
Verlag:John Wiley & Sons Ltd.
Erste Seite:1
Letzte Seite:16
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten
Freie Schlagwörter:Fatigue; Machine learning; Micro-computed tomography; Powder bed fusion of metals using a laser beam; Quality assurance
Themenfelder/Aktivitätsfelder der BAM:Material
Material / Additive Fertigung
DOI:10.1111/ffe.14375
ISSN:8756-758X
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:10.07.2024
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:10.07.2024
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