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Hydrogen-assisted cracking: A deep learning approach for fractographic analysis

  • Hydrogen handling equipment suffers from interaction with their operating environment, which degrades the mechanical properties and compromises component integrity. Hydrogen-assisted cracking is responsible for several industrial failures with potentially severe consequences. A thorough failure analysis can determine the failure mechanism, locate its origin, and identify possible root causes to avoid similar events in the future. Postmortem fractographic analysis can classify the fracture mode and determine whether the hydrogen-metal interaction contributed to the component’s breakdown. Experts in fracture classification identify characteristic marks and textural features by visual inspection to determine the failure mechanism. Although widely adopted, this process is time-consuming and influenced by subjective judgment and individual expertise. This study aims to automate fractographic analysis through advanced computer vision techniques. Different materials were tested in hydrogenHydrogen handling equipment suffers from interaction with their operating environment, which degrades the mechanical properties and compromises component integrity. Hydrogen-assisted cracking is responsible for several industrial failures with potentially severe consequences. A thorough failure analysis can determine the failure mechanism, locate its origin, and identify possible root causes to avoid similar events in the future. Postmortem fractographic analysis can classify the fracture mode and determine whether the hydrogen-metal interaction contributed to the component’s breakdown. Experts in fracture classification identify characteristic marks and textural features by visual inspection to determine the failure mechanism. Although widely adopted, this process is time-consuming and influenced by subjective judgment and individual expertise. This study aims to automate fractographic analysis through advanced computer vision techniques. Different materials were tested in hydrogen atmospheres and inert environments, and their fracture surfaces were analyzed by scanning electron microscopy to create an extensive image dataset. A pre-trained Convolutional Neural Network was finetuned to accurately classify brittle and ductile fractures. In addition, Grad-CAM interpretability method was adopted to identify the image regions most influential in the model’s prediction and compare the saliency maps with expert annotations. This approach offered a reliable data-driven alternative to conventional fractographic analysis.zeige mehrzeige weniger

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Metadaten
Autor*innen:Alessandro Campari, Florian Konert, Nima Razavi, Oded SobolORCiD, Antonio Alvaro
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Computational Materials Science
Jahr der Erstveröffentlichung:2026
Organisationseinheit der BAM:9 Komponentensicherheit
9 Komponentensicherheit / 9.1 Komponenten für Energieträger
Veröffentlichende Institution:Bundesanstalt für Materialforschung und -prüfung (BAM)
Verlag:Elsevier Science
Verlagsort:Amsterdam [u.a.]
Jahrgang/Band:262
Aufsatznummer:114366
Erste Seite:1
Letzte Seite:16
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Angewandte Physik
Freie Schlagwörter:Failure analysis; Fractographic analysis; Hydrogen embrittlement; Material compatibility
Themenfelder/Aktivitätsfelder der BAM:Energie
Energie / Wasserstoff
DOI:10.1016/j.commatsci.2025.114366
URN:urn:nbn:de:kobv:b43-646610
ISSN:0927-0256
Verfügbarkeit des Dokuments:Datei für die Öffentlichkeit verfügbar ("Open Access")
Lizenz (Deutsch):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
Datum der Freischaltung:12.11.2025
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:08.12.2025
Schriftenreihen ohne Nummerierung:Wissenschaftliche Artikel der BAM
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