Imam, Habiba Zahir, Zheng, Yufan, Martinez Rodriguez, Pablo and Ahmad, Rafiq (2021) Vision-Based Damage Localization Method for an Autonomous Robotic Laser Cladding Process. Procedia CIRP, 104. pp. 827-832. ISSN 2212-8271
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Abstract
Currently, damage identification and localization in remanufacturing is a manual visual task. It is time-consuming, labour-intensive. and can result in an imprecise repair. To mitigate this, an automatic vision-based damage localization method is proposed in this paper that integrates a camera in a robotic laser cladding repair cell. Two case studies analyzing different configurations of Faster Region-based Convolutional neural networks (R-CNN) are performed. This research aims to select the most suitable configuration to localize the wear on damaged fixed bends. Images were collected for testing and training the R-CNN and the results of this study indicated a decreasing trend in training and validation losses and a mean average precision (mAP) of 88.7%.
| Item Type: | Article |
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| Additional Information: | Funding information: We express our gratitude to Group Six Technologies Inc. for their assistance and technical support. We also express our appreciation to Mario A. Soriano from the Laboratory of Intelligent Manufacturing, Design and Automation (LIMDA) for sharing his wisdom during the research. 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. |
| Uncontrolled Keywords: | Remanufacturing, Deep neural networks, Damage localization, Robot laser cladding, Machine vision, Repair |
| Subjects: | H700 Production and Manufacturing Engineering |
| Department: | Faculties > Engineering and Environment > Mechanical and Construction Engineering |
| Depositing User: | John Coen |
| Date Deposited: | 08 Dec 2021 16:25 |
| Last Modified: | 08 Dec 2021 16:30 |
| URI: | http://nrl.northumbria.ac.uk/id/eprint/47937 |
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