nach oben

Journal of Nondestructive Evaluation

Erschienen in:

01.03.2024

Simulation-Trained Neural Networks for Automatable Crack Detection in Magnetic Field Images

verfasst von: Tino Band, Benedikt Karrasch, Markus Patzold, Chia-Mei Lin, Ralph Gottschalg, Kai Kaufmann

Erschienen in: Journal of Nondestructive Evaluation | Ausgabe 1/2024

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Magnetic field measurements play a vital role in various industries, particularly in the detection of cracks using magnetic field images, also known as magnetic field leakage testing. This paper presents an approach to automate the extraction of crack signals in magnetic field imaging by using neural networks. The proposed method relies on simulation-based training using the lightweight Python library Magpylib to calculate the three-dimensional static magnetic field of permanent magnets with surface defects. This approach has numerous advantages. It allows control of training data set variance by tuning simulation input parameters such as sample magnetization, measurement parameters, and defect properties to cover a wide range of cracks in size and position. Starting data acquisition before system operation allows investigating potential changes in sample shape or measurement parameters. Importantly, simulation-based data generation eliminates the need for physical measurements, leading to significant time savings. The study presents and discusses results obtained on two different ferromagnetic samples with surface cracks, a hollow cylinder and a steel sheet.

Vorheriger Artikel Approach for Automatic Defect Detection in Aluminum Casting X-Ray Images Using Deep Learning and Gain-Adaptive Multi-Scale Retinex

Nächster Artikel Statistical Analysis and Automation Through Machine Learning of Resonant Ultrasound Spectroscopy Data from Tests Performed on Complex Additively Manufactured Parts

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Lausch, D., Patzold, M., Rudolph, M., Lin, C.-M., Froebel, J., Kaufmann, K.: Magnetic field imaging (MFI) of solar modules. In: 35th European Photovoltaic Solar Energy Conference and Exhibition, pp. 1060, https://doi.org/10.4229/35thEUPVSEC20182018-5BO.11.5 (2018)

Kaufmann, K., Lausch, D., Lin, C.-M., Rudolph, M., Hahn, D., Patzold, M.: Evaluation of the quality of solder joints within silicon solar modules using magnetic field imaging. Phys. Status Solidi A 218(6), 2000292 (2020). https://doi.org/10.1002/pssa.202000292ADSCrossRef

Weber, J., Hoffmann, S., Kaufmann, K., De Rose, A.L.: Magnetic field imaging (MFI) of shingle solar modules. In: 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC), pp. 0231, https://doi.org/10.1109/PVSC48317.2022.9938644 (2022)

Brauchle, F., Grimsmann, F., von Kessel, O., Birke, K.P.: Defect detection in lithium ion cells by magnetic field imaging and current reconstruction. J. Power. Sources 558, 232587 (2023). https://doi.org/10.1016/j.jpowsour.2022.232587CrossRef

Bason, M.G., Coussens, T., Withers, M., Abel, C., Kendall, G., Krüger, P.: Non-invasive current density imaging of lithium-ion batteries. J. Power. Sources 533, 231312 (2022). https://doi.org/10.1016/j.jpowsour.2022.231312CrossRef

Lee, M., Shin, Y., Chang, H., Jin, D., Lee, H., Lim, M., Seo, J., Band, T., Kaufmann, K., Moon, J., Lee, Y. M., Lee, H.: Diagnosis of current flow patterns inside fault-simulated li-ion batteries via non-invasive. In: Operando magnetic field imaging. Small Methods, pp. 2300748, https://doi.org/10.1002/smtd.202300748 (2023)

Feng, B., Wu, J., Tu, H., Tang, J., Kang, Y.: A review of magnetic flux leakage nondestructive testing. Materials 15, 7362 (2022). https://doi.org/10.3390/ma15207362ADSCrossRefPubMedPubMedCentral

Pelkner, M., Kreutzbruck, M.: Spin electronics for non destructive testing. In: Nanomagnetism: applications and perspectives, Wiley‐VCH Verlag GmbH & Co. KGaA, 2017, pp. 81–102 https://doi.org/10.1002/9783527698509.ch5.

Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., Pietikäinen, M.: Deep learning for generic object detection: a survey. Int. J. Comput. Vision 128, 261 (2020). https://doi.org/10.1007/s11263-019-01247-4CrossRef

10.

Buehler, K., Kaufmann, K., Patzold, M., Sprenger, M., Schoenfelder, S.: Identifying defects on solar cells using magnetic field measurements and artificial intelligence trained by a finite-element-model. EPJ Photovoltaics 14, 12 (2023). https://doi.org/10.1051/epjpv/2023005ADSCrossRef

11.

Ortner, M., Bandeira, L.G.C.: “Magpylib: a free Python package for magnetic field computation. SoftwareX 11, 100466 (2020). https://doi.org/10.1016/j.softx.2020.100466CrossRef

12.

Tian, Z., Shen, C., He, T.: FCOS: fully convolutional one-stage object detection. arXiv (2019). https://doi.org/10.48550/arXiv.1904.01355CrossRef

13.

Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. arXiv (2016). https://doi.org/10.48550/arXiv.1506.01497CrossRef

14.

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv (2020). https://doi.org/10.48550/arXiv.2010.11929CrossRef

15.

Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. arXiv (2020). https://doi.org/10.48550/arXiv.2005.12872CrossRef

16.

Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Xie, T., Fang, J., Lorna, I., Yifu, Z., Wong, C., Montes, A.V.D., Wang, Z., Fati, C., Nadar, J.: Laughing, ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation (v7.0), Zenodo, https://doi.org/10.5281/zenodo.7347926 (2022)

17.

Wu, Y., Kirillov, A., Massa, F., Lo, W.-Y., Girshick, R.: Detectron2, https://github.com/facebookresearch/detectron2 (2019)

18.

Zaidi, S.S.A., Ansari, M.S., Aslam, A., Kanwal, N., Asghar, M., Lee, B.: A survey of modern deep learning based object detection models. Digital Signal Process. 126, 103514 (2022). https://doi.org/10.1016/j.dsp.2022.103514CrossRef

19.

Pelkner, M., Neubauer, A., Reimund, V., Kreutzbruck, M., Schütze, A.: Routes for GMR-sensor design in non-destructive testing. Sensors 12(9), 12169 (2012). https://doi.org/10.3390/s120912169ADSCrossRefPubMedCentral

20.

Li, E., Kang, Y., Tang, J., Wu, J., Yan, X.: Analysis on spatial spectrum of magnetic flux leakage using fourier transform. IEEE Trans. Magn. 54(8), 6201810 (2018). https://doi.org/10.1109/TMAG.2018.2844220CrossRef

21.

Pelkner, M., Pohl, R., Kreutzbruck, M., Commandeur, C.: Development of adapted GMR-probes for automated detection of hidden defects in thin steel sheets. AIP Conf. Proc. 1706(1), 020018 (2016). https://doi.org/10.1063/1.4940464CrossRef

22.

Malago, P., Slanovc, F., Herzog, S., Lumetti, S., Schaden, T., Pellegrinetti, A., Moridi, M., Abert, C., Suess, D., Ortner, M.: Magnetic position system design method applied to three-axis joystick motion tracking. Sensors 20(23), 6873 (2020). https://doi.org/10.3390/s20236873ADSCrossRefPubMedPubMedCentral

Titel: Simulation-Trained Neural Networks for Automatable Crack Detection in Magnetic Field Images
verfasst von: Tino Band
Benedikt Karrasch
Markus Patzold
Chia-Mei Lin
Ralph Gottschalg
Kai Kaufmann
Publikationsdatum: 01.03.2024
Verlag: Springer US
Erschienen in: Journal of Nondestructive Evaluation / Ausgabe 1/2024
Print ISSN: 0195-9298
Elektronische ISSN: 1573-4862
DOI: https://doi.org/10.1007/s10921-023-01034-9

Premium Partner

Marktübersichten

Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.

Zur Marktübersicht

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 1/2024

Evaluation Method for Electromagnetic Induction Testing of Dielectrics Using Impedance Plane Diagrams Drawn Using Ampère-Maxwell Equation and Simple Electrical Circuit Model

Monitoring of Large-Amplitude Cyclic Cable Tension via Resonance-Enhanced Magnetoelastic Effect

Enhancement of Track Damage Identification by Data Fusion of Vibration-Based Image Representation

Detecting Defects in Composites Using Combined Heating/Cooling: Theory and Experiments

Correction: Application of Iterative Elastic SH Reverse Time Migration to Synthetic Ultrasonic Echo Data

Statistical Analysis and Automation Through Machine Learning of Resonant Ultrasound Spectroscopy Data from Tests Performed on Complex Additively Manufactured Parts

Premium Partner

Marktübersichten