Interfacial Gap Prediction in Laser Welding of Pure Copper Overlap Joints Using Multiple Sensors
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| Publicado en: | Materials vol. 18, no. 22 (2025), p. 5189-5205 |
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| Otros Autores: | , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3275541482 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1996-1944 | ||
| 024 | 7 | |a 10.3390/ma18225189 |2 doi | |
| 035 | |a 3275541482 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231532 |2 nlm | ||
| 100 | 1 | |a Kim Hyeonhee |u Flexible Manufacturing R&D Department, Korea Institute of Industrial Technology, Incheon 21999, Republic of Korea; 2080khh@gmail.com | |
| 245 | 1 | |a Interfacial Gap Prediction in Laser Welding of Pure Copper Overlap Joints Using Multiple Sensors | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 651 | 4 | |a United States--US | |
| 653 | |a Machine learning | ||
| 653 | |a Quality assessment | ||
| 653 | |a Lap joints | ||
| 653 | |a Cameras | ||
| 653 | |a Accuracy | ||
| 653 | |a Lasers | ||
| 653 | |a Sensors | ||
| 653 | |a Melting | ||
| 653 | |a Aluminum | ||
| 653 | |a Keyholes | ||
| 653 | |a Copper | ||
| 653 | |a Photonics | ||
| 653 | |a Multisensor applications | ||
| 653 | |a Laser beam welding | ||
| 653 | |a Deep learning | ||
| 653 | |a Optical measuring instruments | ||
| 653 | |a Optics | ||
| 653 | |a Geometry | ||
| 653 | |a Multisensor fusion | ||
| 653 | |a Melt pools | ||
| 700 | 1 | |a Kim Cheolhee |u Department of Mechanical and Materials Engineering, Portland State University, Portland, OR 97229, USA | |
| 700 | 1 | |a Kang, Minjung |u Flexible Manufacturing R&D Department, Korea Institute of Industrial Technology, Incheon 21999, Republic of Korea; 2080khh@gmail.com | |
| 773 | 0 | |t Materials |g vol. 18, no. 22 (2025), p. 5189-5205 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275541482/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3275541482/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275541482/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |