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%.
Original language | English |
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Pages (from-to) | 827-832 |
Number of pages | 6 |
Journal | Procedia CIRP |
Volume | 104 |
Early online date | 26 Nov 2021 |
DOIs | |
Publication status | Published - 2021 |
Externally published | Yes |
Event | 54th CIRP Conference on Manufacturing Systems 2021 : "Towards Digitalized Manufacturing 4.0" - Virtual Duration: 22 Sept 2021 → 24 Sept 2021 http://cirp-cms2021.org/ |
Keywords
- Remanufacturing
- Deep neural networks
- Damage localization
- Robot laser cladding
- Machine vision
- Repair