A supervised learning technique for programming a welding arm robot using vision system | ||||
| Engineering Research Journal | ||||
| Article 6, Volume 162, Issue 0, June 2019, Page 86-101 PDF (1.12 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/erj.2019.139804 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Mohamed Hosni Mohamed Ali* 1; Mostafa Rostom Atia1; Farid Abdel Aziz Tolbah2 | ||||
| 1Mechanical Engineering Department, Arab Academy for Science, Technology & Maritime Transport, Cairo, Egypt | ||||
| 2Mechatronics Engineering Department, Ain Shams University, Cairo, Egypt | ||||
| Abstract | ||||
| The programming of the welding robot is a challenging problem, especially with complex paths. Extracting path points and suitable welding speed at every path zone is a complicated, time wasting, and costly process. Moreover, the accuracy of extracting these data at the design stage is affected by the inaccuracies in prewelding processes. This paper introduces a new supervised learning technique for programming a 4 degree of freedom (DOF) welding arm robot with automatic feeding electrode. In this technique, a three-dimensional (3D) machine vision system is developed to grasp the welding position and speed of a complex path by monitoring of an expert welding instructor. Then, these data are used to generate the robot move program. The proposed technique includes fewer steps and hence less consumed time than the conventional one. Moreover, it does not need an expert programmer. From the accuracy point of view, there is no significant difference between the two techniques. These enhancements will improve the share of robots in welding and similar industries. | ||||
| Keywords | ||||
| Supervised learning; Robotic arm; Machine Vision | ||||
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