Simulation-Trained Neural Networks for Automatable Crack Detection in Magnetic Field Images
Author:
Funder
German Federal Ministry for Economic Affairs and Climate Action
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10921-023-01034-9.pdf
Reference22 articles.
1. 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)
2. 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.202000292
3. 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)
4. 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.2022.232587
5. 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.2022.231312
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