Real-time K-TIG welding penetration prediction on embedded system using a segmentation-LSTM model

doi:10.1007/s40436-023-00437-1

Advances in Manufacturing ›› 2023, Vol. 11 ›› Issue (3): 444-461.doi: 10.1007/s40436-023-00437-1

Real-time K-TIG welding penetration prediction on embedded system using a segmentation-LSTM model

Yong-Hua Shi¹, Zi-Shun Wang¹, Xi-Yin Chen¹, Yan-Xin Cui¹, Tao Xu¹, Jin-Yi Wang¹

1 School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510006, China

收稿日期:2022-05-25 修回日期:2022-07-05 出版日期:2023-09-09 发布日期:2023-09-09
通讯作者: Yong-Hua Shi,E-mail:yhuashi@scut.edu.cn E-mail:yhuashi@scut.edu.cn
作者简介:Yong-Hua Shi (1973.12) male, Guangzhou City, Guangdong Province, China. Dr. Shi is a Full Professor in 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, Master's degree, and Ph. D. from 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 was a visiting scholar at the University of Kentucky in 2017. He joined South China University of Technology in 2005. His areas of research are underwater welding, robotic welding, sensing and control technology in manufacturing, and additive manufacturing. He has published more than 100 research articles in reputed international journals and conference proceedings.
Zi-Shun Wang (1997.06) male, Guangzhou City, Guangdong Province, China. Ph.D. candidate, Guangdong Provincial Engineering Research Center for Special Welding Technology and Equipment, South China University of Technology (SCUT). His research direction is vision based welding process monitoring.
Xi-Yin Chen (1991.01) male, Guangzhou City, Guangdong Province, China. Ph.D. candidate, Guangdong Provincial Engineering Research Center for Special Welding Technology and Equipment, South China University of Technology (SCUT). His research is focused on vision of the welding process.
Yan-Xin Cui (1996.09) male, Guangzhou City, Guangdong Province, China. Ph.D., Guangdong Provincial Engineering Research Center for Special Welding Technology and Equipment, South China University of Technology (SCUT). His research direction is multi-sensor based welding process monitoring.
Tao Xu (1993.08) male, Guangzhou City, Guangdong Province, China. Ph.D. candidate, Guangdong Provincial Engineering Research Center for Special Welding Technology and Equipment, South China University of Technology (SCUT). His research is welding quality assessment.
Jin-Yi Wang (1997.03) male, Guangzhou City, Guangdong Province, China. Master candidate, Guangdong Provincial Engineering Research Center for Special Welding Technology and Equipment, South China University of Technology (SCUT). His research is welding process monitoring.
基金资助:
The authors gratefully acknowledge the financial support for this work from the Key Research and Development Program of Guangdong Province (Grant No. 2020B090928003), the National Natural Science Foundation of Guangdong Province (Grant No. 2020A1515011050).

Real-time K-TIG welding penetration prediction on embedded system using a segmentation-LSTM model

Yong-Hua Shi¹, Zi-Shun Wang¹, Xi-Yin Chen¹, Yan-Xin Cui¹, Tao Xu¹, Jin-Yi Wang¹

1 School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510006, China

Received:2022-05-25 Revised:2022-07-05 Online:2023-09-09 Published:2023-09-09
Contact: Yong-Hua Shi,E-mail:yhuashi@scut.edu.cn E-mail:yhuashi@scut.edu.cn
Supported by:
The authors gratefully acknowledge the financial support for this work from the Key Research and Development Program of Guangdong Province (Grant No. 2020B090928003), the National Natural Science Foundation of Guangdong Province (Grant No. 2020A1515011050).

摘要/Abstract

摘要： Keyhole tungsten inert gas (K-TIG) welding is capable of realizing single-sided welding and double-sided forming and has been widely used in medium and thick plate welding. In order to improve the accuracy of automatic weld identification and weld penetration prediction of robot in the process of large workpiece welding, a two-stage model is proposed in this paper, which can monitor the K-TIG welding penetration state in real time on the embedded system, called segmentation-LSTM model. The proposed system extracts 9 weld pool geometric features with segmentation network, and then extracts the weld gap using a traditional algorithm. Then these 10-dimensional features are input into the LSTM model to predict the penetration state, including under penetration, partial penetration, good penetration and over penetration. The recognition accuracy of the proposed system can reach 95.2%. In this system, to solve the difficulty of labeling data and lack of segmentation accuracy, an improved LabelMe capable of live-wire annotation tool and a novel loss function were proposed, respectively. The latter was also called focal dice loss, which enabled the network to achieve a performance of 0.933 mIoU on the testing set. Finally, an improved slimming strategy compresses the network, making the segmentation network achieve real-time on the embedded system (RK3399pro).

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-023-00437-1

关键词: Keyhole tungsten inert gas (K-TIG) welding, Penetration state prediction, Segmentation-LSTM model, Embedded system, Focal dice loss, Improved LabelMe

Abstract: Keyhole tungsten inert gas (K-TIG) welding is capable of realizing single-sided welding and double-sided forming and has been widely used in medium and thick plate welding. In order to improve the accuracy of automatic weld identification and weld penetration prediction of robot in the process of large workpiece welding, a two-stage model is proposed in this paper, which can monitor the K-TIG welding penetration state in real time on the embedded system, called segmentation-LSTM model. The proposed system extracts 9 weld pool geometric features with segmentation network, and then extracts the weld gap using a traditional algorithm. Then these 10-dimensional features are input into the LSTM model to predict the penetration state, including under penetration, partial penetration, good penetration and over penetration. The recognition accuracy of the proposed system can reach 95.2%. In this system, to solve the difficulty of labeling data and lack of segmentation accuracy, an improved LabelMe capable of live-wire annotation tool and a novel loss function were proposed, respectively. The latter was also called focal dice loss, which enabled the network to achieve a performance of 0.933 mIoU on the testing set. Finally, an improved slimming strategy compresses the network, making the segmentation network achieve real-time on the embedded system (RK3399pro).

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-023-00437-1

Key words: Keyhole tungsten inert gas (K-TIG) welding, Penetration state prediction, Segmentation-LSTM model, Embedded system, Focal dice loss, Improved LabelMe

Yong-Hua Shi, Zi-Shun Wang, Xi-Yin Chen, Yan-Xin Cui, Tao Xu, Jin-Yi Wang. Real-time K-TIG welding penetration prediction on embedded system using a segmentation-LSTM model[J]. Advances in Manufacturing, 2023, 11(3): 444-461.

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Real-time K-TIG welding penetration prediction on embedded system using a segmentation-LSTM model

Real-time K-TIG welding penetration prediction on embedded system using a segmentation-LSTM model

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