Zitieren Sie bitte immer diesen URN: urn:nbn:de:kobv:b43-646610
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.…


| 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): | Creative 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 |


