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A lead through approach for programming a welding arm robot using machine vision

Published online by Cambridge University Press:  04 June 2021

Mohamed Hosni Mohamed Ali Open the ORCID record for Mohamed Hosni Mohamed Ali [Opens in a new window]  and
Mostafa Rostom Atia
Mohamed Hosni Mohamed Ali*
Affiliation:
Arab Academy for Science, Technology and Maritime Transport, Sheraton, Cairo, Egypt
Mostafa Rostom Atia
Affiliation:
Arab Academy for Science, Technology and Maritime Transport, Sheraton, Cairo, Egypt
*
*Corresponding author. Email: m-hosni@hotmail.com
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Abstract

Welding is a complex manufacturing process. Its quality depends on the welder skills, especially in welding complex paths. For consistency in modern industries, the arm robot is used to accomplish this task. However, its programming and reprogramming are time consuming and costly and need an expert programmer. These limit the use of robots in medium and small industries. This paper introduces a new supervised learning technique for programming a 4-degree of freedom (DOF) welding arm robot with an automatic feeding electrode. This technique is based on grasping the welding path control points and motion behavior of an expert welder. This is achieved by letting the welder move the robot end effector, which represents the welding torch, through the welding path. At the path control points, the position and speed are recorded using a vision system. Later, these data are retrieved by the robot to replicate the welding path. Several 2D paths are tested to assess the proposed approach accuracy and programming time and easiness in comparison with the common one. The results prove that the proposed approach includes fewer steps and consumes less programming time. Moreover, programming can be accomplished by the welder and no need for an expert programmer. These enhancements will improve the share of robots in welding and similar industries.

Keywords

lead through programmingwelding robotmachine vision
Type
Article
Information
Robotica , Volume 40 , Issue 3 , March 2022 , pp. 464 - 474
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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