Automatic Label Welding Robot System for Bundled Rebars

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%.

Improved YOLOv3 Model for Workpiece Stud Leakage Detection

“…For example, Ren et al [14] proposed the Faster R-CNN algorithm, which effectively speeds up the detection rate. Wang et al [15] proposed an automatic tag welding robot based on a cascaded R-CNN target detection system that automatically identifies and picks up screws for welding.…”