| People | Locations | Statistics |
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| Ferrari, A. |
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| Schimpf, Christian |
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| Dunser, M. |
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| Thomas, Eric |
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| Gecse, Zoltan |
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| Tsrunchev, Peter |
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| Della Ricca, Giuseppe |
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| Cios, Grzegorz |
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| Hohlmann, Marcus |
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| Dudarev, A. |
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| Mascagna, V. |
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| Santimaria, Marco |
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| Poudyal, Nabin |
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| Piozzi, Antonella |
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| Mørtsell, Eva Anne |
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| Jin, S. |
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| Noel, Cédric |
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| Fino, Paolo |
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| Mailley, Pascal |
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| Meyer, Ernst |
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| Zhang, Qi |
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| Pfattner, Raphael | Brussels |
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| Kooi, Bart J. |
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| Babuji, Adara |
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| Pauporte, Thierry |
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Kreutzbruck, Marc
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Publications (8/8 displayed)
- 2023Herausforderungen dickwandiger, duroplastischer Faser-Kunststoff-Verbunde in der Herstellung sowie mechanischen und zerstörungsfreien Prüfung - Ein Reviewcitations
- 2023Fließfrontennachverfolgung in Hochdruck Resin Transfer Molding Werkzeugen mittels Phased Array Ultraschalluntersuchungen
- 2022Untersuchung von dickwandigen GFK-HD-RTM Platten mittels Ultraschalldoppelbrechung
- 2020Integrated defect sensor for the inspection of fiber-reinforced plastics using air-coupled ultrasound
- 2020A mobile nondestructive testing (NDT) system for fast detection of impact damage in fiber-reinforced plastics (FRP)
- 2018Mapping of Deformation-Induced Magnetic Fields in Carbon Steels Using a GMR Sensor Based Metal Magnetic Memory Techniquecitations
- 2018Influence of the microstructure on magnetic stray fields of low-carbon steel weldscitations
- 2017Evaluation of high spatial resolution imaging of magnetic stray fields for early damage detectioncitations
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Mapping of Deformation-Induced Magnetic Fields in Carbon Steels Using a GMR Sensor Based Metal Magnetic Memory Technique
Abstract
Giant magneto-resistive (GMR) sensor based metal magnetic memory (MMM) technique is proposed for mapping of deformation-induced self-magnetic leakage fields (SMLFs) in carbon steel. The specimens were subjected to different amounts of tensile deformation and the deformation-induced SMLFs were measured using a GMR sensor after unloading the specimens. 3D-nonlinear finite element modeling was performed to predict stress–strain state in a steel specimen under tensile load. The experimentally obtained SMLF images were correlated with the finite element model predicted stress–strain states. Studies reveal that the MMM technique can detect the plastic deformation with signal-to-noise ratio better than 20 dB. The technique enables the mapping of plastic deformation in carbon steels for the evaluation of the severity of deformation. The study also reveals that deformation-induced SMLF is influenced by the presence of initial surface residual stress, introduced by shot peening. The intensity of SMLF signal is found to increase with increase in tensile load and decrease with shot peening.