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Automatic Label Welding Robot System for Bundled Rebars
- Source :
- IEEE Access, Vol 9, Pp 160072-160084 (2021)
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- By considering the application requirements of automatic welding of labels on the heads of bundled rebars in the iron and steel industry, an automatic label welding robot system is developed based on machine vision and image processing. The system consists of six modules: pickup unit, image unit, control unit, welding stud unit, label unit, and rebar unit. It can automatically pick up welding studs, pick up labels, perform welding, detect dropped labels, and detect dropped welding studs. The development process of label welding mainly includes the following steps. First, the Cascade RCNN object detection algorithm is used to detect the rebar head, so that the number of rebars and the pixel coordinates of the rebar head centers can be obtained. The detection accuracy of the number of rebars can reach 100%, and the Cascade RCNN average score is 0.9801. Then, an algorithm to identify the center of bundled rebar heads based on the variable-scale method (Davidon–Fletcher–Powell formula (DFP)) is proposed. In addition, limiting conditions, and a process for selection of weldable points for double-label welding are provided. And the coordinate conversion equations of weldable points in different coordinate systems are derived. Finally, a serious of functional verification tests of the robot system were carried out on an actual rebar production line, which verified the accuracy of the weldable point image recognition process, and the usability of the robot system. The results indicate that the accuracy of weldable point recognition can reach 98.99%.
- Subjects :
- General Computer Science
Computer science
Machine vision
Automatic
Rebar
Control unit
Welding
law.invention
Robot welding
rebar head
law
General Materials Science
Computer vision
robot system
business.industry
General Engineering
Process (computing)
Object detection
image processing
TK1-9971
coordinate conversion
Geographic coordinate conversion
Artificial intelligence
Electrical engineering. Electronics. Nuclear engineering
business
location
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....1d3645497e55095f907eb638fc0b15e8