Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Deep learning classification of reproductive tissue from ultrasound: sex determination in red abalone (<i>Haliotis rufescens</i>).

Frontiers in artificial intelligence·2026
Same author

Virtual to Vital: Effectiveness of Telemedicine in Georgia.

Cureus·2026
Same author

Assessing Atopic Dermatitis severity in children from Georgia: the correlation of inflammatory blood markers to the Eczema Area and Severity Index.

Expert review of clinical immunology·2025
Same author

Identifying Factors Predicting Margin Status After Mastectomy.

Annals of surgical oncology·2024
Same author

Impact of routine expert breast pathology consultation and factors predicting discordant diagnosis.

Surgical oncology·2022
Same author

Micro-mechanisms of failure in nano-structured maraging steels characterised through<i>in situ</i>mechanical tests.

Nanotechnology·2022

Related Experiment Video

Updated: Jun 30, 2025

Quantifying the Relative Thickness of Conductive Ferromagnetic Materials Using Detector Coil-Based Pulsed Eddy Current Sensors
06:17

Quantifying the Relative Thickness of Conductive Ferromagnetic Materials Using Detector Coil-Based Pulsed Eddy Current Sensors

Published on: January 16, 2020

5.7K

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

Bernd Wolter1, Benjamin Straß2, Kevin Jacob2

  • 1Fraunhofer Institute for Nondestructive Testing IZFP, 66123, Saarbrücken, Germany. bernd.wolter@izfp.fraunhofer.de.

Scientific Reports
|March 16, 2024
PubMed
Summary
This summary is machine-generated.

Micromagnetic Multiparametric Microstructure and stress Analyser (3MA) and eddy current (EC) methods enable non-destructive evaluation of sheet metals. These techniques characterize material properties, predict formability, and offer marker-free traceability in manufacturing.

More Related Videos

A Novel Method for In Situ Electromechanical Characterization of Nanoscale Specimens
07:15

A Novel Method for In Situ Electromechanical Characterization of Nanoscale Specimens

Published on: June 2, 2017

9.2K
Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

12.2K

Related Experiment Videos

Last Updated: Jun 30, 2025

Quantifying the Relative Thickness of Conductive Ferromagnetic Materials Using Detector Coil-Based Pulsed Eddy Current Sensors
06:17

Quantifying the Relative Thickness of Conductive Ferromagnetic Materials Using Detector Coil-Based Pulsed Eddy Current Sensors

Published on: January 16, 2020

5.7K
A Novel Method for In Situ Electromechanical Characterization of Nanoscale Specimens
07:15

A Novel Method for In Situ Electromechanical Characterization of Nanoscale Specimens

Published on: June 2, 2017

9.2K
Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

12.2K

Area of Science:

  • Materials Science
  • Non-Destructive Evaluation (NDE)
  • Electromagnetism

Background:

  • Sheet metal components require robust identification and material property characterization for quality control.
  • Existing non-destructive evaluation (NDE) methods face challenges in accurately assessing microstructure and stress.
  • Predicting sheet metal formability and ensuring traceability in processing are critical manufacturing concerns.

Purpose of the Study:

  • To present electromagnetic NDE methods for identifying sheet metal components and characterizing their material properties.
  • To investigate the application of Micromagnetic Multiparametric Microstructure and stress Analyser (3MA) for pre-process testing and formability prediction.
  • To develop a marker-free traceability method for sheet metal processing using spatially resolved eddy current (EC) imaging.

Main Methods:

  • Utilized Micromagnetic Multiparametric Microstructure and stress Analyser (3MA), combining multiple micromagnetic NDE techniques.
  • Investigated the influence of probe speed and distance for in-line 3MA application.
  • Employed a spatially resolved eddy current (EC) method to generate intrinsic material microstructure images.
  • Developed a machine learning (ML)-based system for specimen identification using robust features from EC fingerprint images.

Main Results:

  • 3MA enables quantitative analysis of microstructure, mechanical properties, and stress states in ferromagnetic materials.
  • 3MA information can predict sheet metal formability, particularly in cold forming applications.
  • Intrinsic fingerprint images generated by EC methods remain recognizable after plastic deformation and surface coating.
  • A marker-free traceability method was successfully developed using EC imaging and ML for robust specimen identification.

Conclusions:

  • Electromagnetic NDE methods, including 3MA and EC, offer powerful tools for sheet metal evaluation.
  • 3MA provides valuable insights for pre-process testing, enhancing formability prediction.
  • EC-based intrinsic fingerprinting enables reliable, marker-free traceability throughout sheet metal processing.