scholarly journals Linear Mathematical Model for Seam Tracking with an Arc Sensor in P-GMAW Processes

Sensors ◽  
2017 ◽  
Vol 17 (3) ◽  
pp. 591 ◽  
Author(s):  
Wenji Liu ◽  
Liangyu Li ◽  
Ying Hong ◽  
Jianfeng Yue
Author(s):  
Wenji Liu ◽  
Liangyu Li ◽  
Ying Hong ◽  
Jianfeng Yue

Arc sensors have been used in seam tracking and widely studied since the 80s; commercial arc sensing products for T and V shaped grooves have been developed. However, it is difficult to use these arc sensors in narrow gap welding because arc stability and sensing accuracy are not satisfactory. Pulse gas melting arc welding (P-GMAW) has been successfully applied in narrow gap welding and all position welding processes, so it is worthwhile to research P-GMAW arc sensing technology. In this paper, we derived a linear mathematical P-GMAW model for arc sensing, and the assumptions for the model are verified through experiments and finite element methods. Finally, the linear characteristics of the mathematical model were investigated. In torch height changing experiments, uphill experiments, and groove angle changing experiments the P-GMAW arc signals all satisfied the linear rules. In addition, the faster the welding speed, the higher the arc signal sensitivities; the smaller the groove angle, the greater the arc sensitivities. The arc signal variation rate needs to be modified according to the welding power, groove angles, and swing or rotate speed.


2018 ◽  
Vol 21 (6) ◽  
pp. 1407-1412
Author(s):  
Jian-Hui Du ◽  
Jian-Xin Deng ◽  
Ke-Jian Huang ◽  
Jie-Sheng Huang ◽  
Xie Lei

Author(s):  
Yongjae Kim ◽  
Sehun Rhee

Experimental arc sensor models are developed by consideration of the welding conditions and characteristics of each welding process, and developing the model is significant because of its applicability to various welding environments. In this study, different types of regression model were developed for the current area difference method, the current integration difference method, and the weaving end current difference method, which are commonly used for the arc sensor. The characteristics of each regression model were examined, and a multiple-regression model was subsequently suggested, integrating all the conventional model characteristics. The multiple-regression model used the welding current signal of each model as the regressor and the offset distance as the response variable. In addition, an artificial neural network employing the current variable of each model as the input variable and the offset distance as the output variable was suggested as a new arc sensor model. A seam tracking simulation with a fuzzy controller implemented was constituted to facilitate the optimization of the scaling factor, and the scaling factor minimizing the tracking error was determined through the grid search method. Conventional models and the models suggested in this study were consequently compared with each other through the seam tracking experiment using the optimized scaling factors.


2012 ◽  
Vol 26 (4) ◽  
Author(s):  
Nguyễn Thành Luân ◽  
Trần Thiện Phúc ◽  
Nguyễn Duy Anh ◽  
Nguyễn Tân Tiến

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