Vision-Based Damage Localization Method for an Autonomous Robotic Laser Cladding Process

Habiba Zahir Imam, Yufan Zheng, Pablo Martinez, Rafiq Ahmad*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)
1 Downloads (Pure)


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%.
Original languageEnglish
Pages (from-to)827-832
Number of pages6
JournalProcedia CIRP
Early online date26 Nov 2021
Publication statusPublished - 2021
Externally publishedYes
Event54th CIRP Conference on Manufacturing Systems 2021 : "Towards Digitalized Manufacturing 4.0" - Virtual
Duration: 22 Sept 202124 Sept 2021


Dive into the research topics of 'Vision-Based Damage Localization Method for an Autonomous Robotic Laser Cladding Process'. Together they form a unique fingerprint.

Cite this