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

Habiba Zahir Imam, Yufan Zheng, Pablo Martinez Rodriguez, Rafiq Ahmad*, Hamdan Al-Musaibeli

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Article number102452
Number of pages13
JournalRobotics and Computer-Integrated Manufacturing
Volume80
Early online date30 Sep 2022
DOIs
Publication statusE-pub ahead of print - 30 Sep 2022

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