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Image-based defect detection in lithium-ion battery electrode using convolutional neural networks

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Abstract

During the manufacturing of lithium-ion battery electrodes, it is difficult to prevent certain types of defects, which affect the overall battery performance and lifespan. Deep learning computer vision methods were used to evaluate the quality of lithium-ion battery electrode for automated detection of microstructural defects from light microscopy images of the sectioned cells. The results demonstrate that deep learning models are able to learn accurate representations of the microstructure images well enough to distinguish instances with defects from those without defect. Furthermore, the benefits of using pretrained networks for microstructure classification were also demonstrated, achieving the highest classification accuracies. This method provides an approach to analyse thousands of Li-ion battery micrographs for quality assessment in a very short time and it can also be combined with other common battery characterization methods for further technical analysis.

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The raw/processed data required to reproduce these findings cannot be shared at this time due to legal or ethical reasons. The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

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Acknowledgements

The authors acknowledge support by the German Federal Ministry of Education and Research within the program “FH-Impuls” (Project SmartPro, Subproject LiMaProMet, Grant No. 13FH4I02IA).

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Authors and Affiliations

  1. Materials Research Institute, Aalen University, Aalen, Germany

    Olatomiwa Badmos, Andreas Kopp, Timo Bernthaler & Gerhard Schneider

  2. Department of Mechanical Engineering, Karlsruhe Institute of Technology (KIT), 76131, Karlsruhe, Germany

    Olatomiwa Badmos

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  1. Olatomiwa Badmos
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Correspondence to Olatomiwa Badmos.

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Badmos, O., Kopp, A., Bernthaler, T. et al. Image-based defect detection in lithium-ion battery electrode using convolutional neural networks. J Intell Manuf 31, 885–897 (2020). https://doi.org/10.1007/s10845-019-01484-x

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