scholarly journals Weld penetration control with radiographic feedback on weld pool depression

1996 ◽  
Vol 29 (3) ◽  
pp. 186
Keyword(s):  
Weld Pool ◽  
Weld Penetration ◽  
Penetration Control

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%.

Transport Phenomena and Their Effect on Weld Quality in GMA Welding of Aluminum Alloys

Volume 3 ◽  
2004 ◽  
Author(s):  
H. Guo ◽  
H. L. Tsai ◽  
P. C. Wang
Keyword(s):  
Aluminum Alloys ◽  
Weld Pool ◽  
Gma Welding ◽  
Gas Metal Arc Welding ◽  
Welding Current ◽  
Weld Penetration ◽  
Three Dimensional Model ◽  
End Effects ◽  
Welding Arc ◽  
Micro Cracks

Gas metal arc welding (GMAW) of aluminum alloys has recently become popular in the auto industry to increase fuel efficiency of a vehicle. In many situations, the weld is short (say, less than two inches) and the “end effects” become very critical in determining the strength of the weld. At the beginning stage of the welding, when the metal is still “cold”, which is frequently called cold weld, limited weld penetration occurs. On the other hand, at the ending stage of the welding, a “crater” is formed involving micro-cracks and micro-pores. Both the cold weld and the crater can significantly decrease the strength of the weld and are more severe for aluminum alloys as compared to steels. Hence, there are strong needs to improve the GMAW process in order to reduce or eliminate the aforementioned end effects. In this paper, both mathematical modeling and experiments have been conducted to study the beginning stage, ending stage, as well as the quasi-steady-state stage of GMA welding of aluminum alloys. In the modeling, a three-dimensional model using the volume-of-fluid (VOF) method is employed to handle the free surfaces associated with the impingement of droplets into the weld pool and the weld pool dynamics. Transient weld pool shapes and the distributions of temperature and velocity in the weld pool are calculated. The predicted solidified weld bead shapes, including weld penetration and/or reinforcement, are in agreement with experimental results for welds in the aforementioned three stages. It was found that the thickness of the molten weld pool is smaller and there is no vortex developed, as compared to steel welding. The lack of penetration in cold weld is due to the lack of pre-heating by the welding arc. Three techniques are proposed and validated numerically to improve weld penetration by increasing the energy input at the beginning stage of the welding. The crater formation is caused by rapid solidification of the weld pool when the welding arc is terminated. By reducing welding current and reversing the welding direction before terminating the arc, the weld pool is maintained “hot” for a longer time allowing melt flow to fill-up the crater. This method is validated experimentally and numerically to be able to eliminate the formation of the crater and the associated micro-cracks.

Machine Vision Recognition of Weld Pool in Gas Tungsten Arc Welding

1995 ◽  
Vol 209 (2) ◽  
pp. 141-152 ◽  
Author(s):  
R Kovacevic ◽  
Y M Zhang
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Weld Pool ◽  
Visual Information ◽  
Vision System ◽  
Tracking Error ◽  
Seam Tracking ◽  
Front Face ◽  
Welding Parameters ◽  
Weld Penetration

The weld pool and its surrounding area can provide a human welder with sufficient visual information to control welding quality. Seam tracking error and pool geometry can be recognized by a skilled human welder and then utilized to adjust the welding parameters. However, for machine vision, accurate real-time recognition of weld pool geometry is a difficult task due to the high intensity arc light, even though seam tracking errors can be detected. A novel vision system is, therefore, used to acquire quality images against the arc. A real-time recognition algorithm is proposed to analyse the image and recognize the pool geometry based on the pattern recognition technique. Despite surface impurity and other influences, the pool geometry can always be recognized with sufficient accuracy in 150 ms under different welding conditions. To explore the potential application of machine vision in weld penetration control, experiments are conducted to show the correlation between pool geometry and weld penetration state. Thus, pool recognition also provides a possible technique for front-face sensing of the weld penetration.

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