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Sensors
Volume 25
Issue 14
10.3390/s25144346
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Video Segmentation of Wire + Arc Additive Manufacturing (WAAM) Using Visual Large Model

by
Shuo Feng
1,
James Wainwright
1,
Chong Wang
1,
Jun Wang
1,
Goncalo Rodrigues Pardal
1,
Jian Qin
1,*,
Yi Yin
2,
Shakirudeen Lasisi
2,
Jialuo Ding
2 and
Stewart Williams
1
1
Welding and Additive Manufacturing Centre, School of Aerospace, Transport and Manufacturing, Cranfield University, Bedford MK40 3AA, UK
2
WAAM3D Limited, Thornton Chase, Linford Wood, Milton Keynes MK14 6FD, UK
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(14), 4346; https://doi.org/10.3390/s25144346
Submission received: 28 May 2025 / Revised: 6 July 2025 / Accepted: 9 July 2025 / Published: 11 July 2025
(This article belongs to the Special Issue Sensing and Imaging in Computer Vision)
Versions Notes

Abstract

Process control and quality assurance of wire + arc additive manufacturing (WAAM) and automated welding rely heavily on in-process monitoring videos to quantify variables such as melt pool geometry, location and size of droplet transfer, arc characteristics, etc. To enable feedback control based upon this information, an automatic and robust segmentation method for monitoring of videos and images is required. However, video segmentation in WAAM and welding is challenging due to constantly fluctuating arc brightness, which varies with deposition and welding configurations. Additionally, conventional computer vision algorithms based on greyscale value and gradient lack flexibility and robustness in this scenario. Deep learning offers a promising approach to WAAM video segmentation; however, the prohibitive time and cost associated with creating a well-labelled, suitably sized dataset have hindered its widespread adoption. The emergence of large computer vision models, however, has provided new solutions. In this study a semi-automatic annotation tool for WAAM videos was developed based upon the computer vision foundation model SAM and the video object tracking model XMem. The tool can enable annotation of the video frames hundreds of times faster than traditional manual annotation methods, thus making it possible to achieve rapid quantitative analysis of WAAM and welding videos with minimal user intervention. To demonstrate the effectiveness of the tool, three cases are demonstrated: online wire position closed-loop control, droplet transfer behaviour analysis, and assembling a dataset for dedicated deep learning segmentation models. This work provides a broader perspective on how to exploit large models in WAAM and weld deposits.
Keywords: Wire + arc additive manufacturing (WAAM); video segmentation; deep learning; droplet transfer behaviour Wire + arc additive manufacturing (WAAM); video segmentation; deep learning; droplet transfer behaviour

Share and Cite

MDPI and ACS Style

Feng, S.; Wainwright, J.; Wang, C.; Wang, J.; Pardal, G.R.; Qin, J.; Yin, Y.; Lasisi, S.; Ding, J.; Williams, S. Video Segmentation of Wire + Arc Additive Manufacturing (WAAM) Using Visual Large Model. Sensors 2025, 25, 4346. https://doi.org/10.3390/s25144346

AMA Style

Feng S, Wainwright J, Wang C, Wang J, Pardal GR, Qin J, Yin Y, Lasisi S, Ding J, Williams S. Video Segmentation of Wire + Arc Additive Manufacturing (WAAM) Using Visual Large Model. Sensors. 2025; 25(14):4346. https://doi.org/10.3390/s25144346

Chicago/Turabian Style

Feng, Shuo, James Wainwright, Chong Wang, Jun Wang, Goncalo Rodrigues Pardal, Jian Qin, Yi Yin, Shakirudeen Lasisi, Jialuo Ding, and Stewart Williams. 2025. "Video Segmentation of Wire + Arc Additive Manufacturing (WAAM) Using Visual Large Model" Sensors 25, no. 14: 4346. https://doi.org/10.3390/s25144346

APA Style

Feng, S., Wainwright, J., Wang, C., Wang, J., Pardal, G. R., Qin, J., Yin, Y., Lasisi, S., Ding, J., & Williams, S. (2025). Video Segmentation of Wire + Arc Additive Manufacturing (WAAM) Using Visual Large Model. Sensors, 25(14), 4346. https://doi.org/10.3390/s25144346

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