Taotao Pan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Taotao Pan has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Jun Zheng
School of Materials Science and Engineering, Xiamen University of Technology, Xiamen 361021, China
1 shared publication - Wang Zhang
College of Materials Science and Engineering, Xiamen University of Technology, Xiamen 361024, China
1 shared publication - Xiaohong Ge
Key Laboratory of Functional Materials and Application of Fujian Province, Xiamen University of Technology, Xiamen, China
1 shared publication - Yiwen Lai
College of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen 361024, China
1 shared publication - Yuangang Liu
Institute of Pharmaceutical Engineering, Huaqiao University, Xiamen, China
1 shared publication - Zhenqiang Lin
College of Materials Science and Engineering, Xiamen University of Technology, Xiamen 361024, China
1 shared publication
Latest Publications
- Open Access
A New Method for Automatic Detection of Defects in Selective Laser Melting Based on Machine Vision
Selective laser melting (SLM) is a forming technology in the field of metal additive manufacturing. In order to improve the quality of formed parts, it is necessary to monitor the selective laser melting forming process. At present, most of the research on the monitoring of the selective laser melting forming process focuses on the monitoring of the melting pool, but the quality of forming parts cannot be controlled in real-time. As an indispensable link in the SLM forming process, the quality of powder spreading directly affects the quality of the formed parts. Therefore, this paper proposes a detection method for SLM powder spreading defects, mainly using industrial cameras to collect SLM powder spreading surfaces, designing corresponding image processing algorithms to extract three common powder spreading defects, and establishing appropriate classifiers to distinguish different types of powder spreading defects. It is determined that the multilayer perceptron (MLP) is the most accurate classifier. This detection method has high recognition rate and fast detection speed, which cannot only meet the SLM forming efficiency, but also improve the quality of the formed parts through feedback control.
Open AccessA New Method for Automatic Detection of Defects in Selective Laser Melting Based on Machine Vision
Selective laser melting (SLM) is a forming technology in the field of metal additive manufacturing. In order to improve the quality of formed parts, it is necessary to monitor the selective laser melting forming process. At present, most of the research on the monitoring of the selective laser melting forming process focuses on the monitoring of the melting pool, but the quality of forming parts cannot be controlled in real-time. As an indispensable link in the SLM forming process, the quality of powder spreading directly affects the quality of the formed parts. Therefore, this paper proposes a detection method for SLM powder spreading defects, mainly using industrial cameras to collect SLM powder spreading surfaces, designing corresponding image processing algorithms to extract three common powder spreading defects, and establishing appropriate classifiers to distinguish different types of powder spreading defects. It is determined that the multilayer perceptron (MLP) is the most accurate classifier. This detection method has high recognition rate and fast detection speed, which cannot only meet the SLM forming efficiency, but also improve the quality of the formed parts through feedback control.