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

Olatomiwa Badmos (), Andreas Kopp, Timo Bernthaler and Gerhard Schneider
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Olatomiwa Badmos: Aalen University
Andreas Kopp: Aalen University
Timo Bernthaler: Aalen University
Gerhard Schneider: Aalen University

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 4, No 6, 885-897

Abstract: 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.

Keywords: Computer vision; Microstructure; Deep learning; Convolutional neural networks; Lithium-ion battery; Electrode defects; Quality evaluation (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (16)

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DOI: 10.1007/s10845-019-01484-x

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