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

2 Citations (Scopus)


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
Publication statusPublished - 2021
Externally publishedYes
Event54th CIRP Conference on Manufacturing Ssystems, CMS 2021 - Patras, Greece
Duration: 22 Sept 202124 Sept 2021


  • Damage localization
  • Deep neural networks
  • Machine vision
  • Remanufacturing
  • Repair
  • Robot laser cladding

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