Monitoring Weld Pool Surface with Active Vision Image

2020 ◽  
pp. 13-24
Author(s):  
Zongyao Chen ◽  
Zhili Feng ◽  
Jian Chen
Keyword(s):  
Weld Pool ◽  
Active Vision ◽  
Pool Surface ◽  
Weld Pool Surface

scholarly journals Automated Recognition of Weld Pool Characteristics from Active Vision Sensing

2021 ◽  
Vol 100 (5) ◽  
Author(s):  
YONGCHAO CHENG ◽  
◽  
QIYUE WANG ◽  
WENHUA JIAO ◽  
JUN XIAO ◽  
...  
Keyword(s):  
Weld Pool ◽  
Engineering Application ◽  
Welding Process ◽  
Active Vision ◽  
Node Number ◽  
Automated Recognition ◽  
Gas Tungsten Arc ◽  
Pool Surface ◽  
Laser Stripe ◽  
Weld Pool Surface

While penetration occurs underneath the workpiece, the raw information used to detect it during welding must be measurable to a sensor attached to the torch. Challenges are apparent because it is difficult to find such measurable raw information that fundamentally correlates with the phenomena occurring underneath. Additional challenges arise because the welding process is extremely complex such that analytically correlating any raw information to the underneath phenomena is practically impossible; therefore, handcrafted methods to propose features from raw information are human dependent and labor extensive. In this paper, the profile of the weld pool surface was proposed as the raw information. An innovative method was proposed to acquire it by projecting a single laser stripe on the weld pool surface transversely and intercepting its reflection from the mirror-like weld pool surface. To minimize human intervention, which can affect success, a deep-learning-based method was proposed to automatically recognize features from the single-stripe active vision images by fitting a convolutional neural network (CNN). To train the CNN, spot gas tungsten arc welding experiments were designed and conducted to collect the active vision images in pairs with their actual penetration states measured by a camera that views the backside surface of the workpiece. The CNN architecture was optimized by trying different hyperparameters, including kernel number, kernel size, and node number. The accuracy of the optimized model is about 98% and the cycle time in the personal computer is ~ 0.1 s, which fully meets the required engineering application.

A stereo vision method for tracking particle flow on the weld pool surface

2009 ◽  
Vol 105 (12) ◽  
pp. 123104 ◽  
Author(s):  
C. X. Zhao ◽  
I. M. Richardson ◽  
S. Kenjeres ◽  
C. R. Kleijn ◽  
Z. Saldi
Keyword(s):  
Weld Pool ◽  
Stereo Vision ◽  
Particle Flow ◽  
Pool Surface ◽  
Weld Pool Surface

The oscillation of stationary weld pool surface in the GTA welding

2018 ◽  
Vol 256 ◽  
pp. 57-68 ◽  
Author(s):  
J.K. Huang ◽  
M.H. Yang ◽  
J.S. Chen ◽  
F.Q. Yang ◽  
Y.M. Zhang ◽  
...  
Keyword(s):  
Weld Pool ◽  
Gta Welding ◽  
Pool Surface ◽  
Weld Pool Surface

scholarly journals Influence of Pilot Gas Composition on Convective Pattern of Weld Pool Surface in Plasma Keyhole Arc Welding

2017 ◽  
Vol 35 (2) ◽  
pp. 98s-102s ◽  
Author(s):  
Van Anh Nguyen ◽  
Shinichi Tashiro ◽  
Bui Van Hanh ◽  
Manabu Tanaka
Keyword(s):  
Weld Pool ◽  
Arc Welding ◽  
Gas Composition ◽  
Pool Surface ◽  
Convective Pattern ◽  
Weld Pool Surface

scholarly journals Material Flow Behavior on Weld Pool Surface in Plasma Arc Welding Process Considering Dominant Driving Forces

2020 ◽  
Vol 10 (10) ◽  
pp. 3569 ◽  
Author(s):  
Manh Ngo Huu ◽  
Anh Nguyen Van ◽  
Tuan Nguyen Van ◽  
Dang Tran Hai ◽  
Thanh Nguyen Van ◽  
...  
Keyword(s):  
Weld Pool ◽  
Material Flow ◽  
Arc Welding ◽  
Shear Force ◽  
Driving Forces ◽  
Flow Behavior ◽  
Shielding Gas ◽  
Pool Surface ◽  
Marangoni Force ◽  
Weld Pool Surface

In this study, the effect of oxygen in the shielding gas on the material flow behavior of the weld pool surface was discussed to clarify the dominant driving weld pool force in keyhole plasma arc welding (KPAW). To address this issue, the convection flow on the top surface of weld pool was observed using a high-speed video camera. The temperature distribution on the surface along keyhole wall was measured using the two-color pyrometry method to confirm the Marangoni force activity on the weld pool. The results show that the inclination angle of the keyhole wall (keyhole shape) increased especially near the top surface due to the decrease in the surface tension of weld pool through surface oxidation when a shielding gas of Ar + 0.5% O2 was used. Due to the change in the keyhole shape, the upward and backward shear force compositions created a large inclination angle at the top surface of the keyhole. From the temperature measurement results, the Marangoni force was found to alter the direction when 0.5% O2 was mixed with the shielding gas. The shear force was found to be the strongest force among the four driving forces. The buoyant force and Lorentz force were very weak. The Marangoni force was stronger than the Lorentz force but was weaker than shear force. The interaction of shear force and Marangoni force controlled the behavior and speed of material flow on the weld pool surface. A strong upward and backward flow was observed in the case of mixture shielding gas, whereas a weak upward flow was observed for pure Ar. The heat transportation due to the weld pool convection significantly changed when only a small amount of oxygen was admixed in the shielding gas. The results can be applied to control the penetration ratio in KPAW.

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