Nondestructive material characterization and component identification in sheet metal processing with electromagnetic methods.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
15 Mar 2024
Historique:
received: 13 03 2023
accepted: 29 02 2024
medline: 16 3 2024
pubmed: 16 3 2024
entrez: 16 3 2024
Statut: epublish

Résumé

Electromagnetic methods for non-destructive evaluation (NDE) are presented, with which sheet metal components can be identified and their material properties can be characterized. The latter is possible with 3MA, the Micromagnetic Multiparametric Microstructure and stress Analyser. This is a combination of several micromagnetic NDE methods that make it possible to analyse the microstructure in a ferromagnetic material and to determine quantitative values of the mechanical material properties or the stress state. In the case of cold forming, the 3MA application for pre-process testing of sheet metal is discussed. Based on the 3MA information, the formability of the sheets can be predicted. To apply 3MA in-line, the influence of the relative speed and the relative distance between the 3MA probe head and the sheet was investigated. In a second study, a spatially resolved eddy current (EC) method was used to create an image of the intrinsic material microstructure of a component for its identification and traceability. It turned out, that these intrinsic fingerprint images can still be recognized even after subsequent plastic deformation or coating of the surface. This enabled the development of a marker-free traceability method for sheet metal processing. It is based on a low-cost array sensor and a specimen identification using robust and partly redundant features of the fingerprint images processed by machine learning (ML).

Identifiants

pubmed: 38491055
doi: 10.1038/s41598-024-55927-4
pii: 10.1038/s41598-024-55927-4
pmc: PMC10943064
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

6274

Informations de copyright

© 2024. The Author(s).

Références

IEEE Trans Pattern Anal Mach Intell. 2015 Sep;37(9):1904-16
pubmed: 26353135
IEEE Trans Pattern Anal Mach Intell. 2017 Jun;39(6):1137-1149
pubmed: 27295650
J Big Data. 2021;8(1):101
pubmed: 34306963

Auteurs

Bernd Wolter (B)

Fraunhofer Institute for Nondestructive Testing IZFP, 66123, Saarbrücken, Germany. bernd.wolter@izfp.fraunhofer.de.

Benjamin Straß (B)

Fraunhofer Institute for Nondestructive Testing IZFP, 66123, Saarbrücken, Germany.

Kevin Jacob (K)

Fraunhofer Institute for Nondestructive Testing IZFP, 66123, Saarbrücken, Germany.

Markus Rauhut (M)

Fraunhofer Institute for Industrial Mathematics ITWM, 67663, Kaiserslautern, Germany.

Thomas Stephani (T)

Fraunhofer Institute for Industrial Mathematics ITWM, 67663, Kaiserslautern, Germany.

Matthias Riemer (M)

Fraunhofer Institute for Machine Tools and Forming Technology IWU, 09126, Chemnitz, Germany.

Marko Friedemann (M)

Fraunhofer Institute for Machine Tools and Forming Technology IWU, 09126, Chemnitz, Germany.

Classifications MeSH