A novel weld-pool-length monitoring method based on pixel analysis in plasma arc additive manufacturing

doi:10.1007/s40436-023-00466-w

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (2): 335-348.doi: 10.1007/s40436-023-00466-w

• • 上一篇

A novel weld-pool-length monitoring method based on pixel analysis in plasma arc additive manufacturing

Bao-Ri Zhang¹, Yong-Hua Shi²

1 Bionic Robot Research Department, Ji Hua Laboratory, Foshan 528200, Guangdong, People's Republic of China;
2 Guangdong Provincial Engineering Research Center for Special Welding Technology and Equipment, School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, People's Republic of China

收稿日期:2023-02-20 修回日期:2023-05-12 发布日期:2024-05-16
通讯作者: Yong-Hua Shi,E-mail:yhuashi@scut.edu.cn E-mail:yhuashi@scut.edu.cn
作者简介:Bao-Ri Zhang (1994.07) male, Foshan City, Guangdong Province, China. Dr. Zhang is a researcher in the Bionic Robot Research Department at Ji Hua Laboratory (JHL). He received his undergraduate degree and Ph.D. from South China University of Technology in 2017 and 2021, respectively. In 2020, he was a visiting scholar at the Cranfield University in Britain. He joined the Ji Hua Laboratory in 2022. His research focuses on the machine vision of the welding process and control technology in manufacturing, and additive manufacturing. He has published five research articles in reputable international journals and conference proceedings;
Yong-Hua Shi (1973.12) male, Guangzhou City, Guangdong Province, China. Dr. Shi is a full professor at the School of Mechanical and Automotive Engineering at the South China University of Technology, and is the head of the Department of Mechatronic Engineering. He received his undergraduate degree, M.S. degree, and Ph.D. from the South China University of Technology in 1994, 1998, and 2001, respectively. He was a postdoctoral researcher at the Korea Advanced Institute of Science and Technology (KAIST) in 2004, and a visiting scholar at the University of Kentucky in 2017. He joined the South China University of Technology in 2005. His research areas include additive manufacturing, underwater welding, robotic welding, sensing, and control technology in manufacturing. He has published more than 100 research articles in international journals and conference proceedings.
基金资助:
The authors are grateful for the financial support provided by the China Scholarship Council and Basic and Applied Basic Research Foundation of Guangdong Province(Grant No.2022A1515110733).The Cranfield University and the Ji Hua Laboratory are also gratefully acknowledged.

A novel weld-pool-length monitoring method based on pixel analysis in plasma arc additive manufacturing

Bao-Ri Zhang¹, Yong-Hua Shi²

1 Bionic Robot Research Department, Ji Hua Laboratory, Foshan 528200, Guangdong, People's Republic of China;
2 Guangdong Provincial Engineering Research Center for Special Welding Technology and Equipment, School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, People's Republic of China

Received:2023-02-20 Revised:2023-05-12 Published:2024-05-16
Contact: Yong-Hua Shi,E-mail:yhuashi@scut.edu.cn E-mail:yhuashi@scut.edu.cn
Supported by:
The authors are grateful for the financial support provided by the China Scholarship Council and Basic and Applied Basic Research Foundation of Guangdong Province(Grant No.2022A1515110733).The Cranfield University and the Ji Hua Laboratory are also gratefully acknowledged.

摘要/Abstract

摘要： The real-time monitoring of the weld pool during deposition is important for automatic control in plasma arc additive manufacturing. To obtain a high deposition accuracy, it is essential to maintain a stable weld pool size. In this study, a novel passive visual method is proposed to measure the weld pool length. Using the proposed method, the image quality was improved by designing a special visual system that employed an endoscope and a camera. It also includes pixel brightness-based and gradient-based algorithms that can adaptively detect feature points at the boundary when the weld pool geometry changes. This algorithm can also be applied to materials with different solidification characteristics. Calibration was performed to measure the real weld pool length in world coordinates, and outlier rejection was performed to increase the accuracy of the algorithm. Additionally, tests were carried out on the intersection component, and the results showed that the proposed method performed well in tracking the changing weld pool length and was applicable to the real-time monitoring of different types of materials.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-023-00466-w

关键词: Plasma arc additive manufacturing (PAAM), Weld pool geometry, Gradient analysis, Real-time detection

Abstract: The real-time monitoring of the weld pool during deposition is important for automatic control in plasma arc additive manufacturing. To obtain a high deposition accuracy, it is essential to maintain a stable weld pool size. In this study, a novel passive visual method is proposed to measure the weld pool length. Using the proposed method, the image quality was improved by designing a special visual system that employed an endoscope and a camera. It also includes pixel brightness-based and gradient-based algorithms that can adaptively detect feature points at the boundary when the weld pool geometry changes. This algorithm can also be applied to materials with different solidification characteristics. Calibration was performed to measure the real weld pool length in world coordinates, and outlier rejection was performed to increase the accuracy of the algorithm. Additionally, tests were carried out on the intersection component, and the results showed that the proposed method performed well in tracking the changing weld pool length and was applicable to the real-time monitoring of different types of materials.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-023-00466-w

Key words: Plasma arc additive manufacturing (PAAM), Weld pool geometry, Gradient analysis, Real-time detection

Bao-Ri Zhang, Yong-Hua Shi. A novel weld-pool-length monitoring method based on pixel analysis in plasma arc additive manufacturing[J]. Advances in Manufacturing, 2024, 12(2): 335-348.

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A novel weld-pool-length monitoring method based on pixel analysis in plasma arc additive manufacturing

A novel weld-pool-length monitoring method based on pixel analysis in plasma arc additive manufacturing

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