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Solar cell surface defect inspection based on multispectral convolutional neural network

Author

Listed:
  • Haiyong Chen

    (Hebei University of Technology
    Intelligent Rehabilitation Equipment and Detection Technology Engineering Research Center of Ministry of Education)

  • Yue Pang

    (Hebei University of Technology)

  • Qidi Hu

    (Hebei University of Technology)

  • Kun Liu

    (Hebei University of Technology
    Intelligent Rehabilitation Equipment and Detection Technology Engineering Research Center of Ministry of Education)

Abstract

Similar and indeterminate defect detection of solar cell surface with heterogeneous texture and complex background is a challenge of solar cell manufacturing. The traditional manufacturing process relies on human eye detection which requires a large number of workers without a stable and good detection effect. In order to solve the problem, a visual defect detection method based on multi-spectral deep convolutional neural network (CNN) is designed in this paper. Firstly, a selected CNN model is established. By adjusting the depth and width of the model, the influence of model depth and kernel size on the recognition result is evaluated. The optimal CNN model structure is selected. Secondly, the light spectrum features of solar cell color image are analyzed. It is found that a variety of defects exhibited different distinguishable characteristics in different spectral bands. Thus, a multi-spectral CNN model is constructed to enhance the discrimination ability of the model to distinguish between complex texture background features and defect features. Finally, some experimental results and K-fold cross validation show that the multi-spectral deep CNN model can effectively detect the solar cell surface defects with higher accuracy and greater adaptability. The accuracy of defect recognition reaches 94.30%. Applying such an algorithm can increase the efficiency of solar cell manufacturing and make the manufacturing process smarter.

Suggested Citation

  • Haiyong Chen & Yue Pang & Qidi Hu & Kun Liu, 2020. "Solar cell surface defect inspection based on multispectral convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 453-468, February.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:2:d:10.1007_s10845-018-1458-z
    DOI: 10.1007/s10845-018-1458-z
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    References listed on IDEAS

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    1. Francisco G. Bulnes & Ruben Usamentiaga & Daniel F. Garcia & J. Molleda, 2016. "An efficient method for defect detection during the manufacturing of web materials," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 431-445, April.
    2. Ssu-Han Chen & Der-Baau Perng, 2016. "Automatic optical inspection system for IC molding surface," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 915-926, October.
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    Cited by:

    1. Meng Xiao & Bo Yang & Shilong Wang & Yongsheng Chang & Song Li & Gang Yi, 2023. "Research on recognition methods of spot-welding surface appearances based on transfer learning and a lightweight high-precision convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2153-2170, June.
    2. Yunhan Kim & Taekyum Kim & Byeng D. Youn & Sung-Hoon Ahn, 2022. "Machining quality monitoring (MQM) in laser-assisted micro-milling of glass using cutting force signals: an image-based deep transfer learning," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1813-1828, August.
    3. 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.
    4. Zhenying Xu & Ziqian Wu & Wei Fan, 2021. "Improved SSD-assisted algorithm for surface defect detection of electromagnetic luminescence," Journal of Risk and Reliability, , vol. 235(5), pages 761-768, October.
    5. 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.
    6. Shijie Wang & Haiyong Chen & Kun Liu & Ying Zhou & Huichuan Feng, 2023. "Meta-FSDet: a meta-learning based detector for few-shot defects of photovoltaic modules," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3413-3427, December.
    7. Feng Huang & Ben-wu Wang & Qi-peng Li & Jun Zou, 2023. "Texture surface defect detection of plastic relays with an enhanced feature pyramid network," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1409-1425, March.
    8. Zhuxi Ma & Yibo Li & Minghui Huang & Qianbin Huang & Jie Cheng & Si Tang, 2023. "Automated real-time detection of surface defects in manufacturing processes of aluminum alloy strip using a lightweight network architecture," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2431-2447, June.
    9. Chiwu Bu & Tao Liu & Tao Wang & Hai Zhang & Stefano Sfarra, 2023. "A CNN-Architecture-Based Photovoltaic Cell Fault Classification Method Using Thermographic Images," Energies, MDPI, vol. 16(9), pages 1-13, April.
    10. Tae San Kim & Jong Wook Lee & Won Kyung Lee & So Young Sohn, 2022. "Novel method for detection of mixed-type defect patterns in wafer maps based on a single shot detector algorithm," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1715-1724, August.
    11. Aidong Chen & Xiang Li & Hongyuan Jing & Chen Hong & Minghai Li, 2023. "Anomaly Detection Algorithm for Photovoltaic Cells Based on Lightweight Multi-Channel Spatial Attention Mechanism," Energies, MDPI, vol. 16(4), pages 1-15, February.
    12. Danqing Kang & Jianhuang Lai & Junyong Zhu & Yu Han, 2023. "An adaptive feature reconstruction network for the precise segmentation of surface defects on printed circuit boards," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3197-3214, October.

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