An adaptive welding method for grooves with position and size errors-Reference-Cited by-同舟云学术

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An adaptive welding method for grooves with position and size errors

Published:2023-11-17 Issue: Volume: Page:
ISSN:0043-2288
Container-title:Welding in the World
language:en
Short-container-title:Weld World

Author:

Wang Wenkai,Shi Yu^ORCID,Li Chunkai,Gu Yufen,Xu Muzhong,Dai Fengxian

Funder

Excelletn Doctoral Program of Gansu Provincial Natural Science Foundation of China

National Natural Science Foundation of China

Major science and technology project of Gansu Province of China

Zhejiang Provincial Natural Science Foundation of China under Grant

Publisher

Springer Science and Business Media LLC

Subject

Metals and Alloys,Mechanical Engineering,Mechanics of Materials

Link

https://link.springer.com/content/pdf/10.1007/s40194-023-01629-w.pdf

Reference15 articles.

1. Xu Y, Wang Z (2021) Visual sensing technologies in robotic welding: recent research developments and future interests. Sens Actuators, A 320:112551. https://doi.org/10.1016/j.sna.2021.112551

2. Lei T, Huang Y, Shao W, Liu W, Rong Y (2020) A tactual weld seam tracking method in super narrow gap of thick plates. Robot Comput-Integr Manuf 62:101864. https://doi.org/10.1016/j.rcim.2019.101864

3. Baek D, Moon HS, Park SH (2017) Development of an automatic orbital welding system with robust weaving width control and a seam-tracking function for narrow grooves. Int J Adv Manuf Technol 93(1):767–777. https://doi.org/10.1007/s00170-017-0562-0

4. Wenji L, Zhenyu G, Xiao J, Li L, Jianfeng Y (2019) Research on the seam tracking of narrow gap P-GMAW based on arc sound sensing. Sens Actuators, A 292:205–216. https://doi.org/10.1016/j.sna.2019.04.015

5. Zhu J, Wang J, Su N, Xu G, Yang M (2017) An infrared visual sensing detection approach for swing arc narrow gap weld deviation. J Mater Process Technol 243:258–268. https://doi.org/10.1016/j.jmatprotec.2016.12.029

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1. Predictive analysis and feature extraction of weld penetration in P-GMAW based on interpretable deep learning;Journal of Manufacturing Processes;2024-08

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