Digital object identifier for additively manufactured parts as software package

  • A method to uniquely identify samples without printed or handwritten labels is an advantage not just for additively manufactured parts. To kickstart industry use cases, it is also important to provide a ready-made implementation kit. Following an open-science and open-source software approach Germanys Federal Institute for Materials Research and Testing (BAM) seeks to promote digital solutions of ongoing research projects. With this software package a novel method based on microstructural features as identifiers – DOI4AM (digital object identifier for additively manufactured parts) – will be explained alongside its implementation as open-source Python software package. The digital object identifier (DOI) links product data clearly and forgery-proof with real components. Its implementation helps to identify and securely authenticate additively manufactured components during its product life cycle by using characteristic microstructure features - just like a fingerprint. To calculate theA method to uniquely identify samples without printed or handwritten labels is an advantage not just for additively manufactured parts. To kickstart industry use cases, it is also important to provide a ready-made implementation kit. Following an open-science and open-source software approach Germanys Federal Institute for Materials Research and Testing (BAM) seeks to promote digital solutions of ongoing research projects. With this software package a novel method based on microstructural features as identifiers – DOI4AM (digital object identifier for additively manufactured parts) – will be explained alongside its implementation as open-source Python software package. The digital object identifier (DOI) links product data clearly and forgery-proof with real components. Its implementation helps to identify and securely authenticate additively manufactured components during its product life cycle by using characteristic microstructure features - just like a fingerprint. To calculate the DOI fingerprint, a few preprocessing steps need to be performed to detect the uniquely distributed microstructure features that occur during the 3D printing process. A go-through guide shows the preprocessing steps that include CT image capturing, feature segmentation, and data distribution with CSV files. While all steps can be followed along in a Jupyter notebook, the software package includes an application for creating and checking of previously created fingerprints, as well, as a containerized API (application programming interface) service for implementation in existing software platforms or workflows. While data visualization is crucial to understanding the methodology and an essential tool to check for data correctness, an implementation in an industry use case needs to be slim and resource efficient. Therefor the software’s API can be used as an independent service. The project's industry partner proofs its first successful implementation in their digital product passport web solution PASS-X.zeige mehrzeige weniger

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Metadaten
Autor*innen:Heike QuosdorfORCiD
Koautor*innen:Anja WaskeORCiD, Ulrich Jahnke, Kolja Ferlemann, Matthias Habdank
Dokumenttyp:Vortrag
Veröffentlichungsform:Präsentation
Sprache:Englisch
Jahr der Erstveröffentlichung:2025
Organisationseinheit der BAM:6 Materialchemie
6 Materialchemie / 6.0 Abteilungsleitung und andere
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten
Freie Schlagwörter:Additive Manufacturing; Authentication; Digital fingerprint; Digital object identifier; Non-destructive testing; Open Science; Open Source Software; Unique identification; X-ray Computed Tomography
Themenfelder/Aktivitätsfelder der BAM:Material
Material / Additive Fertigung
Veranstaltung:AI MSE 2025
Veranstaltungsort:Bochum, Germany
Beginndatum der Veranstaltung:18.11.2025
Enddatum der Veranstaltung:19.11.2025
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:05.01.2026
Referierte Publikation:Nein
Eingeladener Vortrag (wissenschaftliche Konferenzen):Nein
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