Design of Plane Curvilinear Welding Manipulator Control System Based on Motion Controller

Li, Ru Xiong; Jiao, Song Hua; Yan, Weisheng

doi:10.4028/www.scientific.net/amr.580.122

Cited by 2 publications

(2 citation statements)

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“…In GMAW process for Weld Data Acquisition system like Cloos Qineo data, Smart CPS, Fronius Weldcube EWM X Netng Power Source, Lorch Q-Sys/ Data, Kemppi WeldEye, ESAB Weldcloud / Flow cloud/cloud Services, Linde Avanto can be used for real time data monitoring and controlling [16]. Development of monitoring system equipped with multi-sensory system in the welding process can help to characterise welding defect and process parameter variation, cost, and enhance the reliability of the welded components, cut down the energy cost by reducing energy consumption [17].…”

Section: Arti Cial Intelligence Techniques In Weldingmentioning

confidence: 99%

Machine learning algorithm comparison for IoT enabled Submerged Arc Welding Process monitoring

Barot,

Patel

2023

Preprint

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Traditional weld quality monitoring is replaced by IoT enabled remote automation, which reduces rejection and cost due to defective welded products. Multi-sensory based IoT enabled submerged arc wedding process monitoring helps to track live process parameters and scope for data analytics resulting in optimized weld quality. In present research article IoT enabled SAW process monitoring system is used to record process parameter with relevant weld bead characteristic and correlated for desired weld performance and quality. Process parameters of welding region is transferred to IoT enabled user-friendly mobile and computer dashboard using developed with the help of open source MQTT protocol and HIVEMQ platform. Supervised learning algorithm for weld penetration, weld bead width and reinforcement height are analysed with combination of arc current, arc voltage, wire feed rate and weld speed variations. To meet different customer requirement accuracy level of prediction is tested using optimum number of epoch to learns the patterns of input is performed for adaptive weld control strategies for given data set. Prediction of weld bead dimension are carried out using Supervised Machine Learning (SML) algorithms such as Linear regression, Random forest and Decision tree are compared from externally supplied instances to produce general hypotheses, which ca be useful for development of intelligent systems. Random forest algorithm evaluated highest level of accuracy of prediction and lowest mean absolute error for given data set of IoT enabled SAW process data set.

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Section: Arti Cial Intelligence Techniques In Weldingmentioning

confidence: 99%

Machine learning algorithm comparison for IoT enabled Submerged Arc Welding Process monitoring

Barot,

Patel

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Guo et al 24 presented a new feedback control method of all position welding robot system based on visual sensor, which was verified based on Visual Studio platform. Based on GT-SG-400 motion controller, Li et al 25 developed a kind of three-degree-of-freedom planar curved seam automatic welder, making sure the gesture demand of welding gun perpendicular to welder all the time. Based on the automatic control algorithm of PD controller and enhanced learning controller, the trajectory planning and tracking in the welding process of welding manipulators are realized, and the tracking errors of the manipulator joint are obviously reduced.…”

Section: Introductionmentioning

confidence: 99%

Error allocation model and coordination control for stringer–skin T-shape structure welding robot with multiple manipulators

Zeng

Liu

2022

International Journal of Advanced Robotic Systems

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A new kind of 18 degrees of freedom gantry robot with multiple manipulators could be used to complete the double-sided laser beam welding of stringer–skin T-shape structure for aircraft skin. The coordination control combined with error allocation model analysis is proposed to improve the stability and accuracy of the robot in this article. Firstly, the error model of the stringer–skin T-shape structure welding robot end effectors is analyzed. The score of the robot end effectors position error caused by each joint is obtained. Secondly, according to the path-points transformation matrix of the local coordinate system relative to the base coordinate system, the desired position of stringer–skin T-shape structure welding robot end effectors is calculated to be the input of the control model aiming at the dual-beam laser welding process. Then, the double closed-loop control strategy is proposed for stringer–skin T-shape structure welding robot coordinated motion on the basis of the end error analysis results and proportional-integralderivative (PID) method. Finally, ADAMS and MATLAB are used to establish a co-simulation platform to verify the effectiveness of the proposed coordination control strategy.

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Design of Plane Curvilinear Welding Manipulator Control System Based on Motion Controller

Cited by 2 publications

References 0 publications

Machine learning algorithm comparison for IoT enabled Submerged Arc Welding Process monitoring

Machine learning algorithm comparison for IoT enabled Submerged Arc Welding Process monitoring

Error allocation model and coordination control for stringer–skin T-shape structure welding robot with multiple manipulators

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