Adaptive Nontransferred Plasma Charge Sensor and Its Applications

2006 ◽  
Vol 129 (1) ◽  
pp. 180-189 ◽  
Author(s):  
Wei Lu ◽  
Y. M. Zhang ◽  
John Emmerson
Keyword(s):  
Control Systems ◽  
Weld Pool ◽  
Arc Welding ◽  
Welding Process ◽  
Work Piece ◽  
Plasma Arc Welding ◽  
Sensor Performance ◽  
Pool Surface ◽  
Pipe Welding ◽  
Surface Sensing

Practical welding control systems require durable/compact sensors to sense the welding process and appropriate control algorithms to produce smooth welds. A novel arc welding sensor, referred to as nontransferred plasma charge sensor, which requires no additional attachment to the torch, has been proved to be reliable for weld pool surface sensing. Aiming at eliminating the effect of manufacturing conditions on the sensor performance, this paper proposes two simple yet effective methods. Specifically, reference signals are sampled either from the bottom or the top surface of the work-piece and used to define relative signals, which can measure the depth of the weld pool with better accuracy. Using improved sensing methods, two groups of welding control experiments, keyhole plasma arc welding and all-position pipe welding, have been conducted, and the effectiveness of the developed sensing/control systems in producing quality welds under various conditions is verified.

scholarly journals Relationship among Welding Defects with Convection and Material Flow Dynamic Considering Principal Forces in Plasma Arc Welding

Metals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1444
Author(s):  
Huu Loc Nguyen ◽  
Anh Van Nguyen ◽  
Han Le Duy ◽  
Thanh-Hai Nguyen ◽  
Shinichi Tashiro ◽  
...  
Keyword(s):  
Weld Pool ◽  
Material Flow ◽  
Arc Welding ◽  
Welding Current ◽  
Plasma Arc Welding ◽  
Plasma Arc ◽  
Flow Dynamic ◽  
Pool Surface ◽  
Welding Defects ◽  
Marangoni Force

The material flow dynamic and velocity distribution on the melted domain surface play a crucial role on the joint quality and formation of welding defects. In this study, authors investigated the effects of the low and high currents of plasma arc welding on the material flow and thermodynamics of molten pool and its relationship to the welding defects. The high-speed video camera (HSVC) was used to observe the convection of the melted domain and welded-joint appearance. Furthermore, to consider the Marangoni force activation, the temperature on the melted domain was measured by a thermal HSVC. The results revealed that the velocity distribution on the weld pool surface was higher than that inside the molten weld pool. Moreover, in the case of 80 A welding current, the convection speed of molten was faster than that in other cases (120 A and 160 A). The serious undercut and humping could be seen on the top surface (upper side) and unstable weld bead was visualized on the back side (bottom surface). In the case of 160 A welding current, the convection on the weld pool surface was much more complex in comparison with 80 A and 120 A cases. The excessive convex defect at the bottom side and the concave defect at the top surface were observed. In the case of 120 A welding current, two convection patterns with the main flow in the backward direction were seen. Almost no welding defect could be found. The interaction between the shear force and Marangoni force played a solid state on the convection and heat transportation processes in the plasma arc welding process.

scholarly journals Numerical Analysis of Keyhole and Weld Pool Behaviors in Ultrasonic-Assisted Plasma Arc Welding Process

Materials ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 703
Author(s):  
Junnan Qiao ◽  
Chuansong Wu ◽  
Yongfeng Li
Keyword(s):  
Shear Stress ◽  
Weld Pool ◽  
Arc Welding ◽  
Acoustic Radiation ◽  
Radiation Force ◽  
Acoustic Radiation Force ◽  
Plasma Arc Welding ◽  
Plasma Arc ◽  
Arc Pressure ◽  
Ultrasonic Assisted

The acoustic radiation force driving the plasma jet and the ultrasound reflection at the plasma arc-weld pool interface are considered to modify the formulas of gas shear stress and plasma arc pressure on the anode surface in ultrasonic-assisted plasma arc welding (U-PAW). A transient model taking into account the dynamic changes of heat flux, gas shear stress, and arc pressure on the keyhole wall is developed. The keyhole and weld pool behaviors are numerically simulated to predict the heat transfer and fluid flow in the weld pool and dynamic keyhole evolution process. The model is experimentally validated. The simulation results show that the acoustic radiation force increases the plasma arc velocity, and then increases both the plasma arc pressure and the gas shear stress on the keyhole wall, so that the keyholing capability is enhanced in U-PAW.

scholarly journals Deep Learning-Based Detection of Penetration from Weld Pool Reflection Images

2020 ◽  
Vol 99 (9) ◽  
pp. 239s-245s
Author(s):  
CHAO LI ◽  
◽  
QIYUE WANG ◽  
WENHUA JIAO ◽  
MICHAEL JOHNSON ◽  
...  
Keyword(s):  
Weld Pool ◽  
Data Augmentation ◽  
Welding Process ◽  
Descent Method ◽  
Machine Learning Techniques ◽  
Gradient Descent Method ◽  
Neural Net ◽  
Weld Penetration ◽  
Process Data ◽  
Pool Surface

An innovative method was proposed to determine weld joint penetration using machine learning techniques. In our approach, the dot-structured laser images reflected from an oscillating weld pool surface were captured. Experienced welders typically evaluate the weld penetration status based on this reflected laser pattern. To overcome the challenges in identifying features and accurately processing the images using conventional machine vision algorithms, we proposed the use the raw images without any processing as the input to a convolutional neural network (CNN). The labels needed to train the CNN were the measured weld penetration states, obtained from the images on the backside of the workpiece as a set of discrete weld penetration categories. The raw data, images, and penetration state were generated from extensive experiments using an automated robotic gas tungsten arc welding process. Data augmentation was performed to enhance the robustness of the trained network, which led to 270,000 training examples, 45,000 validation examples, and 45,000 test examples. A six-layer convolutional neural net-work trained with a modified mini-batch gradient descent method led to a final testing accuracy of 90.7%. A voting mechanism based on three continuous images increased the classification accuracy to 97.6%.

scholarly journals Numerical simulation of weld pool formation in submerged arc welding process

2020 ◽  
Vol 38 (4) ◽  
pp. 355-362
Author(s):  
Yosuke OGINO ◽  
Masahiro IIDA ◽  
Satoru ASAI ◽  
Shohei KOZUKI ◽  
Naoya HAYAKAWA ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Weld Pool ◽  
Arc Welding ◽  
Welding Process ◽  
Submerged Arc Welding ◽  
Arc Welding Process ◽  
Pool Formation ◽  
Submerged Arc

Determining Keyhole Stability for Plasma Arc Welding Process Control

1999 ◽  
Author(s):  
Shaobin Zhang ◽  
YuMing Zhang
Keyword(s):  
Arc Welding ◽  
Imaging System ◽  
Welding Process ◽  
The State ◽  
Stability Factor ◽  
Plasma Arc Welding ◽  
Plasma Arc ◽  
Upper Limit ◽  
The Stability ◽  
Burn Through

Abstract Stability of the keyhole plays a fundamental role in producing quality welds in keyhole plasma arc welding. Currently, keyhole size is assumed to be a measurement of its stability. To verify this idea, keyhole and weld pool were simultaneously imaged from the opposite side of the welding torch. Experimental results revealed that the width of the keyhole was not correlative with the stability of the keyhole: as long as the keyhole mode was maintained, the width remained constant despite the changes in the welding current and speed. However, it can be used to estimate the lower limit of the pool width for preventing keyhole collapse. Also, the upper limit of the pool width for preventing burn-through is approximately fixed for given applications. Hence, in this study, pool width, its upper limit, and keyhole width were used to determine the margins of the process from collapse and burn-through. To measure the state of the stability of the keyhole process, the stability distance and stability factor were proposed. Based on the imaging system used, the state of the stability can be monitored in real-time.

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