Intelligent Computed Tomography-based Detection Method for Lithium Battery Mylar Film Damage
With the rapid development and innovation of the lithium battery industry in recent years, battery safety performance testing has become increasingly important. As an essential component of lithium batteries, Mylar films can significantly improve the safety of lithium batteries. However, few studies...
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| Format: | Article |
| Language: | English |
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Editorial Office of Computerized Tomography Theory and Application
2025-07-01
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| Series: | CT Lilun yu yingyong yanjiu |
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| Online Access: | https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2025.061 |
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| _version_ | 1839633628357722112 |
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| author | Menglei LI Dimeng XIA Guoyang LIN Shusen ZHAO |
| author_facet | Menglei LI Dimeng XIA Guoyang LIN Shusen ZHAO |
| author_sort | Menglei LI |
| collection | DOAJ |
| description | With the rapid development and innovation of the lithium battery industry in recent years, battery safety performance testing has become increasingly important. As an essential component of lithium batteries, Mylar films can significantly improve the safety of lithium batteries. However, few studies have focused on damage detection in Mylar films. To address this issue, this study developed an innovative intelligent detection method for lithium battery Mylar film damage. This method utilizes computed tomography (CT) nondestructive testing technology to accurately obtain internal information on lithium batteries. Subsequently, by combining image-preprocessing techniques and deep learning algorithms, an intelligent detection model was constructed to efficiently and accurately detect defective batteries. Experimental results demonstrate that the proposed method achieves a high detection rate and low false-detection rate for Mylar film defects, highlighting its significant potential for practical applications. |
| format | Article |
| id | doaj-art-9ef62c7be74c44d5a3ea19f5ccaf0a7d |
| institution | Matheson Library |
| issn | 1004-4140 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Editorial Office of Computerized Tomography Theory and Application |
| record_format | Article |
| series | CT Lilun yu yingyong yanjiu |
| spelling | doaj-art-9ef62c7be74c44d5a3ea19f5ccaf0a7d2025-07-11T02:30:23ZengEditorial Office of Computerized Tomography Theory and ApplicationCT Lilun yu yingyong yanjiu1004-41402025-07-0134455155910.15953/j.ctta.2025.0612025-061Intelligent Computed Tomography-based Detection Method for Lithium Battery Mylar Film DamageMenglei LI0Dimeng XIA1Guoyang LIN2Shusen ZHAO3National Center for Applied Mathematics Shenzhen, Southern University of Science and Technology, Shenzhen 518055, ChinaNational Center for Applied Mathematics Shenzhen, Southern University of Science and Technology, Shenzhen 518055, ChinaNational Center for Applied Mathematics Shenzhen, Southern University of Science and Technology, Shenzhen 518055, ChinaNational Center for Applied Mathematics Shenzhen, Southern University of Science and Technology, Shenzhen 518055, ChinaWith the rapid development and innovation of the lithium battery industry in recent years, battery safety performance testing has become increasingly important. As an essential component of lithium batteries, Mylar films can significantly improve the safety of lithium batteries. However, few studies have focused on damage detection in Mylar films. To address this issue, this study developed an innovative intelligent detection method for lithium battery Mylar film damage. This method utilizes computed tomography (CT) nondestructive testing technology to accurately obtain internal information on lithium batteries. Subsequently, by combining image-preprocessing techniques and deep learning algorithms, an intelligent detection model was constructed to efficiently and accurately detect defective batteries. Experimental results demonstrate that the proposed method achieves a high detection rate and low false-detection rate for Mylar film defects, highlighting its significant potential for practical applications.https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2025.061mylar films of lithium batterydefect detectionretinex enhancementimage classification |
| spellingShingle | Menglei LI Dimeng XIA Guoyang LIN Shusen ZHAO Intelligent Computed Tomography-based Detection Method for Lithium Battery Mylar Film Damage CT Lilun yu yingyong yanjiu mylar films of lithium battery defect detection retinex enhancement image classification |
| title | Intelligent Computed Tomography-based Detection Method for Lithium Battery Mylar Film Damage |
| title_full | Intelligent Computed Tomography-based Detection Method for Lithium Battery Mylar Film Damage |
| title_fullStr | Intelligent Computed Tomography-based Detection Method for Lithium Battery Mylar Film Damage |
| title_full_unstemmed | Intelligent Computed Tomography-based Detection Method for Lithium Battery Mylar Film Damage |
| title_short | Intelligent Computed Tomography-based Detection Method for Lithium Battery Mylar Film Damage |
| title_sort | intelligent computed tomography based detection method for lithium battery mylar film damage |
| topic | mylar films of lithium battery defect detection retinex enhancement image classification |
| url | https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2025.061 |
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