期刊
APPLIED SCIENCES-BASEL
卷 9, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/app9061085
关键词
defect detection; polymer lithium-ion battery; convolutional neural network; deep learning; blister defect; flower pollination algorithm
类别
资金
- National Key R&D Program of China [2018YFC0114800]
- Shandong Province Natural Science Foundation [ZR2018MF026]
- University Co-construction Project at Weihai [ITDAZMZ001708]
To ensure the quality and reliability of polymer lithium-ion battery (PLB), automatic blister defect detection instead of manual detection is developed in the production of PLB cell sheets. A convolutional neural network (CNN) based detection method is proposed to detect blister in cell sheets employing cell sheet images. An improved architecture for dense block and a learning method based on optimization of learning rate are discussed. The proposed method was superior to other machine learning based methods when the classification performance and confusion matrix were compared in experiments. The proposed CNN method had the best defect detection performance and real-time performance for industry field application.
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