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Industry applications of identifying spot laser-welded nugget for automatic ESS manufacturing process

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Abstract

Recent advancements in energy storage along with power electronic technology have made battery energy storage systems (ESS) a feasible alternative for current power applications. Battery packs with lithium-ion (Li-ion) pouch cells are the main source of ESS. However, it is a big trouble that improper voltage and impedance of laser welding significantly affect the whole battery module during battery pack manufacturing stages, causing the cell imbalance inside and eventually resulting in a thermal runaway of the battery pack and non-durable use. Importantly, the formation of nuggets welded can be classified as good (GD) or not-good (NG) based on the labels after peeling off the flyer of the Li-ion pouch cell. Interestingly, it is usually standard practice in this industry to include substantial numbers of redundant welds to gain confidence in the structural stability of the welded component. Thus, non-destructive and low-cost detection for identifying the nuggets is absolutely necessary. An effective methodology is motivated and proposed with three procedures for the identification of laser-welded nuggets. At first, the nuggets are detected and separated from a grayscale image. Image features are extracted to train the nugget images on the advanced detector model constructed to identify the GD and NG nuggets. Second, this research develops five models for achieving this purpose of detector; one is called the nugget model developed in the convolutional neural network (CNN) technique, and the others use the transfer learning of the most popular pre-trained models (i.e., InceptionV3, EfficientNet, ResNet, and MobileNet). From the comparative studies, it was found that the ResNet (residual network) model more effectively classifies the fault nuggets with a 100% accuracy rate than the other listed models. Finally, this research has significant application contributions for battery manufacturing industries to produce highly efficient welded nugget products by overcoming the cost-ineffective problems of manual inspection; thus, it further helps this industry simultaneously reduce productive inspection time and increase the manufacturing efficiency of ESS at a lower cost without human intervention than in the past.

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Acknowledgements

The authors sincerely thank the Editor-in-Chief and the anonymous referees for their useful comments and suggestions. Simultaneously, the National Science and Technology Council of Taiwan for grant number NSTC 111-2221-E-167-036-MY2 is gratefully acknowledged for its support to this study.

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Authors and Affiliations

  1. College of Management, National Chin-Yi University of Technology, No. 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung, 411030, Taiwan, Republic of China

    You-Shyang Chen

  2. Department of Electronic Engineering, National Ilan University, No. 1, Sec. 1, Shen-Lung Rd., I-Lan 26047, Yilan City, Taiwan, Republic of China

    Jieh-Ren Chang, Ashraf Mohammad & Fang-Chang Kuo

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  1. You-Shyang Chen
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Y-SC: investigation, visualization, writing—review and editing.

J-RC: conceptualization, methodology, supervision, investigation, writing (review and editing), visualization.

AM: conceptualization, methodology, writing (original draft), visualization.

F-CK: conceptualization, supervision, writing (review and editing), visualization.

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Correspondence to Jieh-Ren Chang.

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Chen, YS., Chang, JR., Mohammad, A. et al. Industry applications of identifying spot laser-welded nugget for automatic ESS manufacturing process. Int J Adv Manuf Technol 130, 2705–2729 (2024). https://doi.org/10.1007/s00170-023-12854-4

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