Weld-penetration-depth estimation using deep learning models and multisensor signals in Al/Cu laser overlap welding

Kang, Su‐Jin; Lee, Ki-Dong; Kang, Minjung; Jang, Yong Hoon; Kim, Cheol-Hee

doi:10.1016/j.optlastec.2023.109179

Cited by 13 publications

(1 citation statement)

References 36 publications

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“…In general, vision monitoring of LW in terms of weld penetration has been extensively investigated in several studies, not only in the context of battery module welding, but also in the context of dissimilar metal welding. Deep learning (DL) has been used to estimate the penetration depth in the case of Al-Cu laser-welded overlap joints [51]. A convolutional neural network fed with coaxial weld pool images and a photodiode signal was used as a regressor, achieving an exceptional mean average error over different power and welding speed levels in predicting the penetration depth.…”

Section: Laser Weldingmentioning

confidence: 99%

Welding Challenges and Quality Assurance in Electric Vehicle Battery Pack Manufacturing

Stavropoulos,

Sabatakakis,

Bikas

2024

Batteries

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Electric vehicles’ batteries, referred to as Battery Packs (BPs), are composed of interconnected battery cells and modules. The utilisation of different materials, configurations, and welding processes forms a plethora of different applications. This level of diversity along with the low maturity of welding designs and the lack of standardisation result in great variations in the mechanical and electrical quality of the joints. Moreover, the high-volume production requirements, meaning the high number of joints per module/BP, increase the absolute number of defects. The first part of this study focuses on associating the challenges of welding application in battery assembly with the key performance indicators of the joints. The second part reviews the existing methods for quality assurance which concerns the joining of battery cells and busbars. Additionally, the second part of this paper identifies the general trends and the research gaps for the most widely adopted welding methods in this domain, while it renders the future directions.

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Section: Laser Weldingmentioning

confidence: 99%