REAL-TIME WELDING TRAJECTORY IDENTIFICATION USING IMAGES IN ROBOTIC MANIPULATORS

dc.contributor.author	Madaliyev Khushnid Bahromjon ogli
dc.date.accessioned	2025-12-29T18:16:33Z
dc.date.issued	2024-11-23
dc.description.abstract	This paper presents a novel approach for real-time trajectory identification of welding paths in robotic manipulators using image processing techniques. The proposed method combines edge detection algorithms, deep learning models, and adaptive control strategies for accurate welding path tracking. Experimental results demonstrate a significant improvement in welding precision, path consistency, and processing speed. Key findings include a 25% increase in trajectory accuracy and a reduction in defect rates.
dc.format	application/pdf
dc.identifier.uri	https://webofjournals.com/index.php/4/article/view/2263
dc.identifier.uri	https://asianeducationindex.com/handle/123456789/25309
dc.language.iso	eng
dc.publisher	Web of Journals Publishing
dc.relation	https://webofjournals.com/index.php/4/article/view/2263/2245
dc.rights	https://creativecommons.org/licenses/by-nc-nd/4.0
dc.source	Web of Technology: Multidimensional Research Journal; Vol. 2 No. 11 (2024): WOT; 291-297
dc.source	2938-3757
dc.subject	Robotic manipulator, real-time trajectory, welding, image processing, deep learning.
dc.title	REAL-TIME WELDING TRAJECTORY IDENTIFICATION USING IMAGES IN ROBOTIC MANIPULATORS
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	Peer-reviewed Article

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