LAPSE:2024.1243
Published Article
LAPSE:2024.1243
A Study on Defect Detection of Dissimilar Joints in Cu-STS Tubes Using Infrared Thermal Imaging of Induction Heating Brazing
June 21, 2024
We proposed a novel detection method for identifying joint defects in the brazing process between copper tubes and stainless steel using a convolutional neural network (CNN) model. The brazing joints were created using high-frequency induction heating equipment, and infrared thermal imaging cameras were employed to capture the thermal data generated during the jointing process. The experiments involved 15.88 mm diameter copper tubes commonly used in plate heat exchangers, stainless-steel tubes, and filler metal containing 20% Ag. The thermal data were obtained with a resolution of 80 × 80 pixels per frame, resulting in 4796 normal joint data and 5437 defective joint data collected over 100 high-frequency induction-heating brazing experiments. A total of 10,233 thermal imaging data were categorized into 6548 training data, 1638 validation data, and 2047 test data for the development of the predictive model. We designed CNN models with varying hyperparameters, specifically the number of kernel filters and nodes, to evaluate their impact on detection performance. A comparative analysis revealed that a CNN model structure, exhibiting 98.53% accuracy and 99.82% recall on test data, was the most effective. The selected CNN-based defect prediction model demonstrated the potential of using CNN models to discern joint defects in tube configurations that are challenging to identify visually. This study opens avenues for applying CNN-based models for detecting imperfections in complex tube structures.
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Keywords
brazing, convolutional neural network, defect identification, high-frequency Induction heating, infrared thermal image
Subject
Suggested Citation
Lee CW, Woo S, Kim J. A Study on Defect Detection of Dissimilar Joints in Cu-STS Tubes Using Infrared Thermal Imaging of Induction Heating Brazing. (2024). LAPSE:2024.1243
Author Affiliations
Lee CW: Automotive Materials & Components R&D Group, Korea Institute of Industrial Technology, Gwangju 61012, Republic of Korea; Department of Metallurgical Engineering, Jeonbuk National University, Baekje-daero, Deokjin-gu, Jeonju-si 54896, Republic of Korea [ORCID]
Woo S: Automotive Materials & Components R&D Group, Korea Institute of Industrial Technology, Gwangju 61012, Republic of Korea
Kim J: Automotive Materials & Components R&D Group, Korea Institute of Industrial Technology, Gwangju 61012, Republic of Korea
Woo S: Automotive Materials & Components R&D Group, Korea Institute of Industrial Technology, Gwangju 61012, Republic of Korea
Kim J: Automotive Materials & Components R&D Group, Korea Institute of Industrial Technology, Gwangju 61012, Republic of Korea
Journal Name
Processes
Volume
12
Issue
1
First Page
163
Year
2024
Publication Date
2024-01-09
ISSN
2227-9717
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Original Submission
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PII: pr12010163, Publication Type: Journal Article
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LAPSE:2024.1243
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https://doi.org/10.3390/pr12010163
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[v1] (Original Submission)
Jun 21, 2024
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Jun 21, 2024
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