Vision-based spatial damage localization method for autonomous robotic laser cladding repair processes

Imam, Habiba Zahir, Al-Musaibeli, Hamdan, Zheng, Yufan, Martinez Rodriguez, Pablo and Ahmad, Rafiq (2023) Vision-based spatial damage localization method for autonomous robotic laser cladding repair processes. Robotics and Computer-Integrated Manufacturing, 80. p. 102452. ISSN 0736-5845

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Official URL: https://doi.org/10.1016/j.rcim.2022.102452

Abstract

Repair technologies have been considered as sustainable approaches due to their capability to restore value in a damaged component and bring it to like-new condition. However, in contrast to a manufacturing process benefiting from an automated environment, the automation level for repair and remanufacturing processes remains low. With the aim of moving the repair industry towards autonomy, this study proposes a novel repair framework. The developed methodology presents a vision-based Robotic Laser Cladding Repair Cell (RLCRC) that has two features: (a) an intelligent inspection system that uses a deep learning model to automatically detect the damaged region in an image; (b) employing computer vision-based calibration and 3D scanning techniques to precisely identify the geometries of damaged area. The repair of fixed bends is selected as the case study. The results obtained validate the efficacy of the proposed framework, enabling automatic damage detection and damaged volume extraction for worn fixed bends. Following the suggested framework, a time reduction of more than 63% is reported.

Item Type: Article
Additional Information: Funding information: The authors acknowledge the Natural Sciences and Engineering Research Council (NSERC), Grant Nos. (NSERC RGPIN-2017-04516 and NSERC CRDPJ 537378-18) for funding this project.
Subjects: G400 Computer Science
G500 Information Systems
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: Rachel Branson
Date Deposited: 06 Oct 2022 09:58
Last Modified: 06 Oct 2022 10:00
URI: https://nrl.northumbria.ac.uk/id/eprint/50301

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