Feature information extraction of hot wire laser metal deposition process based on object and key point detection

LI Chunkai; PAN Yu; SHI Yu; WANG Wenkai; ZHAO Zhongbo

doi:10.12073/j.hjxb.20240711002

TRANSACTIONS OF THE CHINA WELDING INSTITUTION > 2024 > 45(11): 115-120. > DOI: 10.12073/j.hjxb.20240711002

LI Chunkai, PAN Yu, SHI Yu, WANG Wenkai, ZHAO Zhongbo. Feature information extraction of hot wire laser metal deposition process based on object and key point detection[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(11): 115-120. DOI: 10.12073/j.hjxb.20240711002

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Feature information extraction of hot wire laser metal deposition process based on object and key point detection

1.
State Key Laboratory of Advanced Processing and Recycling of Non-ferrous Metals, Lanzhou University of Technology, Lanzhou, 730050, China
2.
Key Laboratory of Non-ferrous Metal Alloys and Processing of State Education Ministry, Lanzhou University of Technology, Lanzhou, 730050, China

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Received Date: July 10, 2024
Available Online: October 23, 2024

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Abstract

Abstract

In order to improve the quality stability and achieve simultaneous real-time monitoring of droplet-melting pool multi-feature information during the hot-wire laser metal deposition (HW-LMD) process, this study adopts a high-precision real-time monitoring method based on a high-dynamic vision camera combined with a YOLO v8 deep learning neural network. The dynamic changes in the HW-LMD process are captured by a camera, and the transition mode and melt pool behaviour are monitored synchronously using the YOLO v8 neural network, which firstly determines whether the deposition process is a stable liquid bridge transition or not, and then extracts the key point information of the melt pool dimensions in the liquid bridge transition mode. The results show that the YOLO v8 neural network has high accuracy in detecting the transition mode and melt pool key point information of the deposition process, with an accuracy rate of 98.8% and 99.9%, respectively, and an average error of 4.1% for the melt pool width, and the inference time is only 12 ms per frame on average, which meets the demand for real-time monitoring of the HW-LMD process.
- hot wire laser metal deposition,
- process monitoring,
- YOLO v8 deep learning neural network,
- melt pool size,
- transition mode

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References

[1]	Abuabiah M, Mbodj N G, Shaqour B, et al. Advancements in laser wire-feed metal additive manufacturing: A brief review[J]. Materials, 2023, 16(5): 2030 − 2053.
[2]	产玉飞, 陈长军, 张敏. 金属增材制造过程的在线监测研究综述[J]. 材料导报, 2019, 33(17): 2839 − 2846. doi: 10.11896/cldb.19010111 Chan Yufei, Chen Changjun, Zhang Min. Review of on-line monitoring research on metal additive manufacturing process[J]. Materials Review, 2019, 33(17): 2839 − 2846. doi: 10.11896/cldb.19010111
[3]	Zhao Y, Yang Q, Huang J, et al. Droplet transfer and weld geometry in laser welding with filling wire[J]. The International Journal of Advanced Manufacturing Technology, 2017, 90(5-8): 2153 − 2161. doi: 10.1007/s00170-016-9486-3
[4]	郑世卿, 温鹏, 单际国. 激光热丝焊接过程焊丝过渡行为及其稳定性的研究[J]. 中国激光, 2014, 41(4): 109 − 116. Zheng Shiqing, Wen Peng, Shan Jiguo. Research on wire transfer and its stability in laser hot wire welding process[J]. Chinese Journal of Lasers, 2014, 41(4): 109 − 116.
[5]	Chang S, Gach S, Senger A, et al. A new 3D printing method based on non-vacuum electron beam technology[J]. Journal of Physics: Conference Series, 2018, 1074(1): 012017.
[6]	Chang S, Zhang H, Xu H, et al. Closed-loop control of droplet transfer in electron-beam free form fabrication[J]. Sensors, 2020, 20(3): 923 − 935.
[7]	Zhang Z, Kong F, Kovacevic R. Laser hot-wire cladding of Co-Cr-W metal cored wire[J]. Optics and Lasers in Engineering, 2020, 128: 105998 − 106010.
[8]	Kisielewicz A, Mi Y, Sikström F, et al. Multi sensor monitoring of the wire-melt pool interaction in hot-wire directed energy deposition using laser beam[J]. IOP Conference Series: Materials Science and Engineering, 2023, 1296(1): 012011. doi: 10.1088/1757-899X/1296/1/012011
[9]	郭忠峰, 刘俊池, 杨钧麟. 基于关键点检测方法的焊缝识别[J]. 焊接学报, 2024, 45(1): 88 − 93. doi: 10.12073/j.hjxb.20230204001 Guo Zhongfeng, Liu Junchi, Yang Junlin. Weld recognition based on key point detection method[J]. Transactions of the China Welding Institution, 2024, 45(1): 88 − 93. doi: 10.12073/j.hjxb.20230204001
[10]	Chandra M, Rajak S, Vimal K E K. Deep learning-based framework for the observation of real-time melt pool and detection of anomaly in wire-arc additive manufacturing[J]. Materials and Manufacturing Processes, 2024, 39(6): 761 − 777. doi: 10.1080/10426914.2023.2254386
[11]	Jocher G, Chaurasia A, Qiu J. YOLO by Ultralytics[Z]. Frederick: Ultralytics, 2023.
[12]	Wang W, Shi Y, Li C, et al. Feature information extraction method for narrow gap U-type groove based on laser vision[J]. Journal of Manufacturing Processes, 2023, 104: 257 − 272. doi: 10.1016/j.jmapro.2023.08.053

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Feature information extraction of hot wire laser metal deposition process based on object and key point detection

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