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

Author

Listed:
  • Olatomiwa Badmos

    (Aalen University
    Karlsruhe Institute of Technology (KIT))

  • Andreas Kopp

    (Aalen University)

  • Timo Bernthaler

    (Aalen University)

  • Gerhard Schneider

    (Aalen University)

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.

Suggested Citation

  • Olatomiwa Badmos & Andreas Kopp & Timo Bernthaler & Gerhard Schneider, 2020. "Image-based defect detection in lithium-ion battery electrode using convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 885-897, April.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:4:d:10.1007_s10845-019-01484-x
    DOI: 10.1007/s10845-019-01484-x
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    Cited by:

    1. Chia-Yu Hsu & Ju-Chien Chien, 2022. "Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 831-844, March.
    2. Simon Müller & Christina Sauter & Ramesh Shunmugasundaram & Nils Wenzler & Vincent Andrade & Francesco Carlo & Ender Konukoglu & Vanessa Wood, 2021. "Deep learning-based segmentation of lithium-ion battery microstructures enhanced by artificially generated electrodes," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    3. Pan, Yue & Kong, Xiangdong & Yuan, Yuebo & Sun, Yukun & Han, Xuebing & Yang, Hongxin & Zhang, Jianbiao & Liu, Xiaoan & Gao, Panlong & Li, Yihui & Lu, Languang & Ouyang, Minggao, 2023. "Detecting the foreign matter defect in lithium-ion batteries based on battery pilot manufacturing line data analyses," Energy, Elsevier, vol. 262(PB).
    4. Yuanyuan Wang & Ling Ma & Lihua Jian & Huiqin Jiang, 2023. "Conductive particle detection via efficient encoder–decoder network," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3563-3577, December.
    5. Swarit Anand Singh & K. A. Desai, 2023. "Automated surface defect detection framework using machine vision and convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1995-2011, April.
    6. Ruiyang Hao & Bingyu Lu & Ying Cheng & Xiu Li & Biqing Huang, 2021. "A steel surface defect inspection approach towards smart industrial monitoring," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1833-1843, October.
    7. Nhat-To Huynh & Duong-Dong Ho & Hong-Nguyen Nguyen, 2023. "An Approach for Designing an Optimal CNN Model Based on Auto-Tuning GA with 2D Chromosome for Defect Detection and Classification," Sustainability, MDPI, vol. 15(6), pages 1-14, March.
    8. Seunghwan Jung & Minseok Kim & Eunkyeong Kim & Baekcheon Kim & Jinyong Kim & Kyeong-Hee Cho & Hyang-A Park & Sungshin Kim, 2024. "The Early Detection of Faults for Lithium-Ion Batteries in Energy Storage Systems Using Independent Component Analysis with Mahalanobis Distance," Energies, MDPI, vol. 17(2), pages 1-23, January.
    9. Omid Davtalab & Ali Kazemian & Xiao Yuan & Behrokh Khoshnevis, 2022. "Automated inspection in robotic additive manufacturing using deep learning for layer deformation detection," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 771-784, March.
    10. Minyoung Lee & Joohyoung Jeon & Hongchul Lee, 2022. "Explainable AI for domain experts: a post Hoc analysis of deep learning for defect classification of TFT–LCD panels," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1747-1759, August.
    11. Feiyang Li & Nian Cai & Xueliang Deng & Jiahao Li & Jianfa Lin & Han Wang, 2022. "Serial number inspection for ceramic membranes via an end-to-end photometric-induced convolutional neural network framework," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1373-1392, June.
    12. Saksham Jain & Gautam Seth & Arpit Paruthi & Umang Soni & Girish Kumar, 2022. "Synthetic data augmentation for surface defect detection and classification using deep learning," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1007-1020, April.
    13. Zeqing Yang & Mingxuan Zhang & Yingshu Chen & Ning Hu & Lingxiao Gao & Libing Liu & Enxu Ping & Jung Il Song, 2024. "Surface defect detection method for air rudder based on positive samples," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 95-113, January.
    14. Shuai Ma & Kechen Song & Menghui Niu & Hongkun Tian & Yunhui Yan, 2024. "Cross-scale fusion and domain adversarial network for generalizable rail surface defect segmentation on unseen datasets," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 367-386, January.
    15. Xinyu Suo & Jian Liu & Licheng Dong & Chen Shengfeng & Lu Enhui & Chen Ning, 2022. "A machine vision-based defect detection system for nuclear-fuel rod groove," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1649-1663, August.

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