Generation of WAAM Defect Images Using a Hybrid CVAE-CGAN: A Data Augmentation Strategy for Small and Imbalanced Datasets
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| Publicado en: | The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings (2025), p. 1-6 |
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The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3253885402 | ||
| 003 | UK-CbPIL | ||
| 024 | 7 | |a 10.1109/CYBER67662.2025.11168313 |2 doi | |
| 035 | |a 3253885402 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 228229 |2 nlm | ||
| 100 | 1 | |a Yang, Junle |u School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong,Wollongong,NSW,Australia,2522 | |
| 245 | 1 | |a Generation of WAAM Defect Images Using a Hybrid CVAE-CGAN: A Data Augmentation Strategy for Small and Imbalanced Datasets | |
| 260 | |b The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |c 2025 | ||
| 513 | |a Conference Proceedings | ||
| 520 | 3 | |a Conference Title: 2025 IEEE 15th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)Conference Start Date: 2025 July 15Conference End Date: 2025 July 18Conference Location: Shanghai, ChinaIn industrial Wire Arc Additive Manufacturing (WAAM)., the scarcity and imbalance of labeled defect images limit the effectiveness of deep learning-based quality inspection systems. This paper presents a hybrid CVAE-CGAN framework designed to generate high-resolution., class-conditional molten pool images for data augmentation in small-sample settings. By combining a conditional variational autoencoder with adversarial training and integrating VGG19-based perceptual loss and sub-pixel convolution, the proposed model produces visually realistic and diverse synthetic defect images. Extensive experiments on a nine-class WAAM defect dataset demonstrate the model's ability to enhance classification performance, especially for underrepresented categories, offering a scalable solution to mitigate data limitations in intelligent manufacturing. | |
| 653 | |a Datasets | ||
| 653 | |a Data augmentation | ||
| 653 | |a Image resolution | ||
| 653 | |a Machine learning | ||
| 653 | |a Defects | ||
| 653 | |a Intelligent manufacturing systems | ||
| 653 | |a Melt pools | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Yuan, Lei |u School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong,Wollongong,NSW,Australia,2522 | |
| 700 | 1 | |a Mu, Haochen |u School of Mechanical and Power Engineering, Nanjing Tech University,Nanjing,China,211816 | |
| 700 | 1 | |a He, Fengyang |u School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong,Wollongong,NSW,Australia,2522 | |
| 700 | 1 | |a Ding, Donghong |u School of Mechanical and Power Engineering, Nanjing Tech University,Nanjing,China,211816 | |
| 700 | 1 | |a Pan, Zengxi |u School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong,Wollongong,NSW,Australia,2522 | |
| 700 | 1 | |a Li, Huijun |u School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong,Wollongong,NSW,Australia,2522 | |
| 773 | 0 | |t The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings |g (2025), p. 1-6 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3253885402/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |