Work

Interfacial Gap Prediction in Laser Welding of Pure Copper Overlap Joints Using Multiple Sensors

Year: 2025

Type: article

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.

Source: Materials

DOI: 10.3390/ma18225189

Authors: Hyeonhee Kim, Cheolhee Kim, Minjung Kang

Institutions: Yonsei University, Korea Institute of Industrial Technology, Portland State University

Language: English

Topic: Welding Techniques and Residual Stresses

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Korea Institute of Industrial Technology