A Review Paper on Vision Based Identification, Detection and Tracking of Weld Seams Path in Welding Robot Environment

Shah, Hairol Nizam Mohd; Sulaiman, Marizan; Shukor, Ahmad Zaki; Jamaluddin, Muhammad Herman; Rashid, Mohd Zamzuri Ab

doi:10.5539/mas.v10n2p83

Cited by 18 publications

(4 citation statements)

References 19 publications

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“…They can be integrated into the robotic welding system in various ways, such as mounting them on the robot’s arm or integrating them into the welding torch itself. The sensors provide real-time information to the robotic system, enabling it to adjust welding parameters such as speed, pressure, and heat input to optimize weld quality and consistency [ 152 ]. Customized approaches are crucial when applying welding robots across different industries.…”

Section: Robotic Welding Sensors In Industrial Applicationsmentioning

confidence: 99%

Visual Sensing and Depth Perception for Welding Robots and Their Industrial Applications

Wang,

Li,

2023

Sensors

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With the rapid development of vision sensing, artificial intelligence, and robotics technology, one of the challenges we face is installing more advanced vision sensors on welding robots to achieve intelligent welding manufacturing and obtain high-quality welding components. Depth perception is one of the bottlenecks in the development of welding sensors. This review provides an assessment of active and passive sensing methods for depth perception and classifies and elaborates on the depth perception mechanisms based on monocular vision, binocular vision, and multi-view vision. It explores the principles and means of using deep learning for depth perception in robotic welding processes. Further, the application of welding robot visual perception in different industrial scenarios is summarized. Finally, the problems and countermeasures of welding robot visual perception technology are analyzed, and developments for the future are proposed. This review has analyzed a total of 2662 articles and cited 152 as references. The potential future research topics are suggested to include deep learning for object detection and recognition, transfer deep learning for welding robot adaptation, developing multi-modal sensor fusion, integrating models and hardware, and performing a comprehensive requirement analysis and system evaluation in collaboration with welding experts to design a multi-modal sensor fusion architecture.

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Section: Robotic Welding Sensors In Industrial Applicationsmentioning

confidence: 99%

Visual Sensing and Depth Perception for Welding Robots and Their Industrial Applications

Wang,

Li,

2023

Sensors

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“…The controller is utilized to program the robot, giving commands to parts of the robot to activate sensors, move, and initiate the welding process to complete the task. The major types of failures in actualizing robots have occurred due to the product that controls the robot [11].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Robotic Welding Based on a Computer Vision Technology Approach

et al. 2022

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Robots have become an essential part of modern industries in welding departments to increase the accuracy and rate of production. The intelligent detection of welding line edges to start the weld in a proper position is very important. This work introduces a new approach using image processing to detect welding lines by tracking the edges of plates according to the required speed by three degrees of a freedom robotic arm. The two different algorithms achieved in the developed approach are the edge detection and top-hat transformation. An adaptive neuro-fuzzy inference system ANFIS was used to choose the best forward and inverse kinematics of the robot. MIG welding at the end-effector was applied as a tool in this system, and the weld was completed according to the required working conditions and performance. The parts of the system work with compatible and consistent performances, with acceptable accuracy for tracking the line of the welding path.

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“…With the development of robot technology, industrial robot welding began to gradually replace manual welding in welding production [9], This represents the second stage of automated welding. Industrial robotic welding can achieve effective welding in places that human welders cannot reach, while achieving overall improvements in welding quality, adaptability and production efficiency [10]. However, most welding robots still work in a "teach and play back" mode.…”

Section: Introductionmentioning

confidence: 99%

Key Technology of Intelligentized Welding Manufacturing and Systems Based on the Internet of Things and Multi-Agent

Liu

Chen

2022

JMMP

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With the development of the Internet of Things (IoT), Big Data, Artificial Intelligence technology, and the emergence of modern information technologies such as intelligent manufacturing, welding systems are changing, and intelligentized welding manufacturing and systems (IWMS) utilizing these technologies are attracting attention from both academia and industry. This paper investigates sensing technology, multi-information sensor fusion technology, feature recognition technology, the quality prediction method, control method, and intelligent welding production line application in the IWMS. Combining IoT technology and multi-agent systems, a hierarchical structure model welding manufacturing system (IoT-MAS) in the form of “leader-following” was constructed. The multi-agent welding manufacturing system has the advantages of distribution, intelligence, internal coordination and so on. The IoT-MAS consists of several sub-agents, which are divided into five categories according to their functions and internal processing logic. Combined with the functions of the intelligent welding manufacturing system, the agent structure of the whole welding process was proposed, and the matching communication technology and algorithm were designed. The intelligent welding manufacturing system based on IoT-MAS proposed in this paper can effectively solve the integrated design problem of large welding manufacturing systems.

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A Review Paper on Vision Based Identification, Detection and Tracking of Weld Seams Path in Welding Robot Environment

Cited by 18 publications

References 19 publications

Visual Sensing and Depth Perception for Welding Robots and Their Industrial Applications

Visual Sensing and Depth Perception for Welding Robots and Their Industrial Applications

Intelligent Robotic Welding Based on a Computer Vision Technology Approach

Key Technology of Intelligentized Welding Manufacturing and Systems Based on the Internet of Things and Multi-Agent

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