Published In
Materials (basel, Switzerland)
Document Type
Article
Publication Date
11-14-2025
Subjects
Laser Welding -- Copper joints
Abstract
In this study, a novel approach was proposed for predicting the interfacial gap in copper overlap joints by using deep learning and multi-sensor fusion. In this method, an image sensor, a spectrometer, and optical sensors tomography (OCT) sensors were used to develop and validate deep learning models under various gap conditions. The results revealed that the variation in melt pool dimensions, changes in keyhole behavior, intensity variations at specific wavelengths, and keyhole depth derived from the OCT data could be used to accurately predict the interfacial gap. Among the proposed models, a binary gap classification model achieved the highest accuracy of 98.8%. The spectrometer was the most effective sensor in this study, whereas the image and OCT sensors provided complementary data. The best performance was achieved by fusing all three sensors, which emphasizes the importance of sensor fusion for precise gap prediction. This study provides valuable insights into improving weld quality assessment and optimizing manufacturing processes.
Rights
Copyright (c) 2025 The Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
DOI
10.3390/ma18225189
Persistent Identifier
https://archives.pdx.edu/ds/psu/44279
Publisher
MDPI AG
Citation Details
Kim, H., Kim, C., & Kang, M. (2025). Interfacial Gap Prediction in Laser Welding of Pure Copper Overlap Joints Using Multiple Sensors. Materials, 18(22), 5189.