Automatic Internal Wrinkles Detection of Lithium-ion Batteries using Convolutional Neural Network

Jianwen Peng; Mingqing Xue; Yunjiang Lou

doi:10.1109/CASE49439.2021.9551649

Automatic Internal Wrinkles Detection of Lithium-ion Batteries using Convolutional Neural Network

Jianwen Peng
, Mingqing Xue
, Yunjiang Lou^*

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

To make sure the quality and reliability of lithium-ion batteries(LIBs) and improve detection speed, developing automatic defects detection to take the place of manual detection has been a general trend in the quality control lines of LIBs. In this paper, a detection method based on X-ray technology and convolutional neural network(CNN) is proposed for internal wrinkles detection in LIBs. Besides, for reducing false positive rate, loss fuction is modified by adding penalty coefficient when training CNN model. The proposed method has a nice performance in accuracy and false positive rate, and satisfies industrial requirements, and has been applied in the quality control of Lithium-ion battery production lines.

Original language	English
Title of host publication	2021 IEEE 17th International Conference on Automation Science and Engineering, CASE 2021
Publisher	IEEE Computer Society
Pages	1422-1427
Number of pages	6
ISBN (Electronic)	9781665418737
DOIs	https://doi.org/10.1109/CASE49439.2021.9551649
State	Published - 23 Aug 2021
Externally published	Yes
Event	17th IEEE International Conference on Automation Science and Engineering, CASE 2021 - Lyon, France Duration: 23 Aug 2021 → 27 Aug 2021

Publication series

Name	IEEE International Conference on Automation Science and Engineering
Volume	2021-August
ISSN (Print)	2161-8070
ISSN (Electronic)	2161-8089

Conference

Conference	17th IEEE International Conference on Automation Science and Engineering, CASE 2021
Country/Territory	France
City	Lyon
Period	23/08/21 → 27/08/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Access to Document

10.1109/CASE49439.2021.9551649

Cite this

Peng, J., Xue, M., & Lou, Y. (2021). Automatic Internal Wrinkles Detection of Lithium-ion Batteries using Convolutional Neural Network. In 2021 IEEE 17th International Conference on Automation Science and Engineering, CASE 2021 (pp. 1422-1427). (IEEE International Conference on Automation Science and Engineering; Vol. 2021-August). IEEE Computer Society. https://doi.org/10.1109/CASE49439.2021.9551649

@inproceedings{f90d09f9aca94bf8bf5269a4d1147583,

title = "Automatic Internal Wrinkles Detection of Lithium-ion Batteries using Convolutional Neural Network",

abstract = "To make sure the quality and reliability of lithium-ion batteries(LIBs) and improve detection speed, developing automatic defects detection to take the place of manual detection has been a general trend in the quality control lines of LIBs. In this paper, a detection method based on X-ray technology and convolutional neural network(CNN) is proposed for internal wrinkles detection in LIBs. Besides, for reducing false positive rate, loss fuction is modified by adding penalty coefficient when training CNN model. The proposed method has a nice performance in accuracy and false positive rate, and satisfies industrial requirements, and has been applied in the quality control of Lithium-ion battery production lines.",

author = "Jianwen Peng and Mingqing Xue and Yunjiang Lou",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 17th IEEE International Conference on Automation Science and Engineering, CASE 2021 ; Conference date: 23-08-2021 Through 27-08-2021",

year = "2021",

month = aug,

day = "23",

doi = "10.1109/CASE49439.2021.9551649",

language = "英语",

series = "IEEE International Conference on Automation Science and Engineering",

publisher = "IEEE Computer Society",

pages = "1422--1427",

booktitle = "2021 IEEE 17th International Conference on Automation Science and Engineering, CASE 2021",

address = "美国",

}

Peng, J, Xue, M & Lou, Y 2021, Automatic Internal Wrinkles Detection of Lithium-ion Batteries using Convolutional Neural Network. in 2021 IEEE 17th International Conference on Automation Science and Engineering, CASE 2021. IEEE International Conference on Automation Science and Engineering, vol. 2021-August, IEEE Computer Society, pp. 1422-1427, 17th IEEE International Conference on Automation Science and Engineering, CASE 2021, Lyon, France, 23/08/21. https://doi.org/10.1109/CASE49439.2021.9551649

Automatic Internal Wrinkles Detection of Lithium-ion Batteries using Convolutional Neural Network. / Peng, Jianwen; Xue, Mingqing; Lou, Yunjiang.
2021 IEEE 17th International Conference on Automation Science and Engineering, CASE 2021. IEEE Computer Society, 2021. p. 1422-1427 (IEEE International Conference on Automation Science and Engineering; Vol. 2021-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automatic Internal Wrinkles Detection of Lithium-ion Batteries using Convolutional Neural Network

AU - Peng, Jianwen

AU - Xue, Mingqing

AU - Lou, Yunjiang

PY - 2021/8/23

Y1 - 2021/8/23

N2 - To make sure the quality and reliability of lithium-ion batteries(LIBs) and improve detection speed, developing automatic defects detection to take the place of manual detection has been a general trend in the quality control lines of LIBs. In this paper, a detection method based on X-ray technology and convolutional neural network(CNN) is proposed for internal wrinkles detection in LIBs. Besides, for reducing false positive rate, loss fuction is modified by adding penalty coefficient when training CNN model. The proposed method has a nice performance in accuracy and false positive rate, and satisfies industrial requirements, and has been applied in the quality control of Lithium-ion battery production lines.

AB - To make sure the quality and reliability of lithium-ion batteries(LIBs) and improve detection speed, developing automatic defects detection to take the place of manual detection has been a general trend in the quality control lines of LIBs. In this paper, a detection method based on X-ray technology and convolutional neural network(CNN) is proposed for internal wrinkles detection in LIBs. Besides, for reducing false positive rate, loss fuction is modified by adding penalty coefficient when training CNN model. The proposed method has a nice performance in accuracy and false positive rate, and satisfies industrial requirements, and has been applied in the quality control of Lithium-ion battery production lines.

UR - https://www.scopus.com/pages/publications/85116963344

U2 - 10.1109/CASE49439.2021.9551649

DO - 10.1109/CASE49439.2021.9551649

M3 - 会议稿件

AN - SCOPUS:85116963344

T3 - IEEE International Conference on Automation Science and Engineering

SP - 1422

EP - 1427

BT - 2021 IEEE 17th International Conference on Automation Science and Engineering, CASE 2021

PB - IEEE Computer Society

T2 - 17th IEEE International Conference on Automation Science and Engineering, CASE 2021

Y2 - 23 August 2021 through 27 August 2021

ER -

Automatic Internal Wrinkles Detection of Lithium-ion Batteries using Convolutional Neural Network

Abstract

Publication series

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this