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J Weld Join > Volume 42(5); 2024 > Article
Journal of Welding and Joining 2024;42(5):514-520.
DOI: https://doi.org/10.5781/JWJ.2024.42.5.8    Published online October 31, 2024.
Quality Classification of Multi-Layer Copper Foil Stacks Welds for Ultrasonic Welding Using CNN and RNN
Seon-myoung Hong1,3  , Jae-woo Cho1  , Hee-seon Bang2 
1Department of Welding and Joining Science Engineering, Chosun University, Graduate school, Gwangju, 61452, Korea
2Department of Welding and Joining Science Engineering, Chosun University, Gwangju, 61452, Korea
3Department of Special Equipment, chunnam Techno University, Gokseong, 57500, Korea
Correspondence:  Jae-woo Cho,
Email: jaewoovs17@naver.com
Received: 31 August 2024   • Revised: 20 September 2024   • Accepted: 25 September 2024
Abstract
This study aims to develop a model for classifying weld quality using data acquired in real-time during the ultrasonic welding process. The data utilized includes LVDT (DP-10, DAQ-Express) and power signal data. The model categorizes welds into three classes (insufficient, sufficient, and excessive) based on the total input energy of the ultrasonic welding process. To classify the quality of ultrasonic welds, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are employed. The focus of this research is on utilizing CNN and RNN models to classify weld quality based on signal data acquired from ultrasonic welding, aiming to enhance the reliability of secondary battery joints.
Key Words: Multilayer copper foil, Ultrasonic welding, Weldability, Classification model, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM)
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ORCID iDs

Seon-myoung Hong
https://orcid.org/0009-0000-3131-3323

Jae-woo Cho
https://orcid.org/0009-0002-4936-8108

Hee-seon Bang
https://orcid.org/0000-0003-4127-3171

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