Xinxin Zeng
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Xinxin Zeng has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Fengtao Wang
Key Laboratory of Intelligent Manufacturing Technology (Shantou University), Ministry of Education, Shantou 515063, People’s Republic of China
1 shared publication - Guiying Chen
Key Laboratory of Intelligent Manufacturing Technology of Ministry of Education, College of Engineering, Shantou University, Shantou 515063, People’s Republic of China
1 shared publication - Jian Tang
Key Laboratory of Intelligent Manufacturing Technology of Ministry of Education, College of Engineering, Shantou University, Shantou 515063, People’s Republic of China
1 shared publication - Jianan Guo
Department of Mechanical Engineering, College of Engineering, Shantou University, Shantou 515063, China
1 shared publication - Shitong Peng
Department of Mechanical Engineering, Shantou University, Shantou 515063, China
1 shared publication
Latest Publications
- 14 October 2024
- Published by IOP Publishing in Measurement Science and Technology
- Vol. 36 (1) , 015201
Classification of melt pool states for defect detection in laser directed energy deposition using FixConvNeXt model
Laser directed energy deposition (L-DED) has emerged as a promising technique for rapid prototyping due to its cost-effectiveness and efficiency. However, the intricate and multi-scale physics of the process hinder its widespread application. This paper addresses the challenge by focusing on real-time identification of melt pool states to detect defects early and minimize resource wastage. To achieve this, a FixConvNeXt model was developed for fast and accurate monitoring of melt pool states. This model was trained using 5000 melt pool images captured during the printing of single-track deposits from a charge-coupled device. To evaluate its performance, FixConvNeXt was compared with other models using various metrics. Experimental results demonstrated that FixConvNeXt achieved superior performance in accurately identifying melt pool states with 99.1% accuracy, while also reducing computation burden and processing time. The mechanism of classification by FixConvNeXt was explained using gradient-weighted class activation mapping. The research findings highlight the potential application of online process monitoring in L-DED. This study lays the foundation for future development of an efficient deep learning network for automatic defect detection and feedback control.
- 14 October 2024
- Published by IOP Publishing in Measurement Science and Technology
- Vol. 36 (1) , 015201
Classification of melt pool states for defect detection in laser directed energy deposition using FixConvNeXt model
Laser directed energy deposition (L-DED) has emerged as a promising technique for rapid prototyping due to its cost-effectiveness and efficiency. However, the intricate and multi-scale physics of the process hinder its widespread application. This paper addresses the challenge by focusing on real-time identification of melt pool states to detect defects early and minimize resource wastage. To achieve this, a FixConvNeXt model was developed for fast and accurate monitoring of melt pool states. This model was trained using 5000 melt pool images captured during the printing of single-track deposits from a charge-coupled device. To evaluate its performance, FixConvNeXt was compared with other models using various metrics. Experimental results demonstrated that FixConvNeXt achieved superior performance in accurately identifying melt pool states with 99.1% accuracy, while also reducing computation burden and processing time. The mechanism of classification by FixConvNeXt was explained using gradient-weighted class activation mapping. The research findings highlight the potential application of online process monitoring in L-DED. This study lays the foundation for future development of an efficient deep learning network for automatic defect detection and feedback control.