The application of fuzzy control in underwater welding seam-tracking system

Author(s):  
Rong-Hua Hu ◽  
Ke-Liang Tu ◽  
Hua Zhang ◽  
Guo-Ping Liu
2014 ◽  
Vol 644-650 ◽  
pp. 845-848
Author(s):  
Fu Yang ◽  
Wen Ming Zhang ◽  
Wan Cai Jiao

It is high difficult to control the underwater welding because of the effect of water and the leak proofness of the weld devices which is a troubling problem. In this paper, a DSP-based automatic seam tracking system for underwater welding is designed. This system has the advantages of simple hardware structure, low-cost, rich function software, friendly human-machine interface, and easily realizing. And the work of this paper can be used for further research in underwater welding seam automatic tracking.


2011 ◽  
Vol 55-57 ◽  
pp. 1759-1763
Author(s):  
Zhong Hu Yuan ◽  
Shuo Jun Yu ◽  
Xiao Wei Han

In the process of weld seam tracking, traditional mathematical model of classical and modern control theory is hard to meet the requirement of high performance controller. This article based on the embedded digital signal processor DSP-TMS320F2812 for the field of industrial automation control.The fuzzy control technology is applied to real-time welding seam-tracking system, according to the F2812 which has the characteristics of real-time multitasking scheduling of resources and then designed the real-time control value adjustment Fuzzy-PI control system. The designed DSP real-time fuzzy control system gives full play to powerful control and signal processing ability of F2812, it can fully adapt for the controlling requirement of super-speed and high-precision.


2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Yanbiao Zou ◽  
Mingquan Zhu ◽  
Xiangzhi Chen

Abstract Accurate locating of the weld seam under strong noise is the biggest challenge for automated welding. In this paper, we construct a robust seam detector on the framework of deep learning object detection algorithm. The representative object algorithm, a single shot multibox detector (SSD), is studied to establish the seam detector framework. The improved SSD is applied to seam detection. Under the SSD object detection framework, combined with the characteristics of the seam detection task, the multifeature combination network (MFCN) is proposed. The network comprehensively utilizes the local information and global information carried by the multilayer features to detect a weld seam and realizes the rapid and accurate detection of the weld seam. To solve the problem of single-frame seam image detection algorithm failure under continuous super-strong noise, the sequence image multifeature combination network (SMFCN) is proposed based on the MFCN detector. The recurrent neural network (RNN) is used to learn the temporal context information of convolutional features to accurately detect the seam under continuous super-noise. Experimental results show that the proposed seam detectors are extremely robust. The SMFCN can maintain extremely high detection accuracy under continuous super-strong noise. The welding results show that the laser vision seam tracking system using the SMFCN can ensure that the welding precision meets industrial requirements under a welding current of 150 A.


2012 ◽  
Vol 442 ◽  
pp. 370-374 ◽  
Author(s):  
Yong Qiang Wu ◽  
Zhong Hu Yuan ◽  
Jia Han Wang

Kinematics model of welding robot is built in the paper. An improved fuzzy controller (Fuzzy-P) for welding robot mobile platform is designed based on analyzing seam tracking control system. The domain of fuzzy control should not be set too big in order to make system smooth, but the system must respond rapidly. P control can respond rapidly. When weld seam deviation is big,it adopts P control while seam deviation is small, it adopts Fuzzy-P control. The simulation result shows that the improved controller is effective for 45°broken line; the welding torch is able to track the welding seam well.


2020 ◽  
Vol 10 (1) ◽  
pp. 324 ◽  
Author(s):  
Jin-Hyeong Park ◽  
Hyeong-Soon Moon

Automatic welding technology is a solution to increase welding productivity and improve welding quality in offshore pipe welding. To increase welding productivity, it is necessary to save time during the assembly/disassembly of the guide track from the welding carriage and pipe to move the next station. The guide track consists of a pneumatic system that does not separate the welding carriage, and two welding carriages operate on a half-pipe joint to increase productivity. These welding carriages automatically operate under the controller command. An automatic welding system consists of a DC motor module, a step motor module, a welding control module, a welding monitoring module, and a central control module. The control systems incorporate control modules and transmit commands to each module for an automatic welding system. In order to minimize the inevitable misalignment between the centerline of the welding seam and the welding torch for each welding pass, a moving average algorithm for seam tracking is proposed, which was proven to be suitable for the root pass, filling pass, and cap pass. Welding experiments were also carried out to verify the validity of the weld seam tracking system.


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