Sensing controlled pulse key-holing condition in plasma arc welding

Jia, Chuanbao; Zhang, Yuming

doi:10.1016/s1003-6326(08)60275-7

Cited by 45 publications

(9 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Quality control in PAW was proposed by monitoring the efflux plasma voltage signals. 16) Using a CCD camera from the bottom side of the workpieces, direct observation of the keyhole was proposed. 17) In the practical field, it was difficult to set up the visual sensor on the bottom side.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control by Convolutional Neural Network in Plasma Arc Welding System

Yamane

Matsuo

2020

ISIJ Int.

View full text Add to dashboard Cite

Plasma arc welding (PAW) was employed in joining thick materials with groove. Due to the high-density plasma arc, keyhole welding was used in the butt welding. The gap might be taken in places due to the heat distortion during the welding. To achieve a high-quality welding, the adaptive control is required according to the gap. The authors tried to apply CMOS camera to obtain information from the top surface and achieve synchronization between the camera shutter and welding current. Thus, a clear image of the weld pool with the keyhole and the gap was taken. The brightness distribution in the front of the weld pool becomes uneven due to the plasma arc. To classify the gap and select the welding conditions, such as the welding current and the plasma gas flow rate, the authors used one of the deep learning algorithms, convolutional neural network (CNN). In the training and testing data, the performance of the CNN was found to be satisfactory. However, if the plasma torch does not trace the welding line, the pool image will be different from the training image. Hence, the CNN will not be able to estimate the correct gap. To avoid this, the authors detected the welding line from the pool image and selected the image area near the welding line as the input of the CNN, i.e., the input area is selected based on the image obtained during the welding. The validity of the proposed method was verified by the welding experiments.

show abstract

Section: Introductionmentioning

confidence: 99%

Adaptive Control by Convolutional Neural Network in Plasma Arc Welding System

Yamane

Matsuo

2020

ISIJ Int.

View full text Add to dashboard Cite

show abstract

“…Many sensing methods are adopted to obtain such information. Some researchers [6][7][8] utilized arc sound, welding voltage and current and other indirect signals obtained in the welding process to detect the welding state. Compared with the nonvisual signal sensing method, the visual signal sensing method has the advantages of high measuring accuracy, large amount of information and noncontact with the work piece, which is widely used in the shape detection of molten pool and keyhole [9,10].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Fusion Hole Perforation Based on Arc Characteristics of Front Image in Backing Welding

Cao

Wang

et al. 2020

Materials

View full text Add to dashboard Cite

In one-side welding with back-formation, the weld is penetrated after the fusion hole is perforated. Therefore, judging whether the fusion hole is perforated is very important to realize autocontrol of penetration in one-side welding with back-formation process. Previous researches mainly focused on the morphological characteristics of the molten pool and fusion hole to judge the weld penetration state. Sometimes it is difficult to obtain the morphological characteristics of the molten pool, keyhole and fusion hole and image processing is complex. In this paper, a visual detection system of fusion holes based on visual sensing is constructed to obtain the real-time fusion hole images in backing welding. It is found that the arc characteristics in the front images contain abundant information about the perforation of fusion hole. An image processing program is developed based on MATLAB software, and the arc characteristic parameters in front images are obtained. Taking the arc characteristic parameters as the input, obtaining the penalty function and the kernel function parameters through the cross validation and grid search method, a prediction model of fusion hole perforation based on the support vector machine is put forward. The accuracy for prediction samples is 88%. By analyzing the misidentified samples, it is found that some of the newly perforated images are predicted as nonperforated ones, which has little influence on the penetration control of the weld.

show abstract

“…Increasing the robustness of the keyhole molten pool is more helpful for the interference resisting. The controlled pulsed plasma arc welding process [7,8], which makes the holes in periodic opening and closing state by adjusting the current output, could effectively improve the robustness of the molten pool. The double-pulsed VPPAW (DP-VPPAW) [9,10] adds additional high-frequency pulsed current into electrode negative (EN) and electrode positive (EP), which makes the molten pool oscillate periodically to improve stability of the welding process and quality.…”

Section: Introductionmentioning

confidence: 99%

A Primary Study of Variable Polarity Plasma Arc Welding Using a Pulsed Plasma Gas

Zhang

Jiang

et al. 2019

Materials

View full text Add to dashboard Cite

A process variant of variable polarity plasma arc welding (VPPAW), that is, the pulsed plasma gas VPPAW process, was developed. The pulsed plasma gas was transmitted into the variable polarity plasma arc through a high-frequency solenoid valve to modify the output of the plasma arc. The collection of arc electrical characteristics, arc shapes, and weld formation from VPPAW, double-pulsed VPPAW (DP-VPPAW), and pulsed plasma gas VPPAW (PPG-VPPAW) was carried out to examine if the pulsed plasma gas was able to play a positive role in improving the stability and quality of the VPPAW process. The arc voltage shows that the pulsed plasma gas had a greater influence on the electrode positive polarity voltage. The lower the plasma gas frequency was, the lower the arc voltage fluctuation frequency was and the greater the arc voltage fluctuation amplitude was. From the arc image, it could be observed that the arc core length had a short decrease during the general rising trend after plasma gas was turned on. The arc core width only had a slight change due to the restriction of the torch orifice. Compared with pulsed current wave, the pulsed plasma gas could better enhance the fluidity of the molten pool to reduce porosity during aluminum keyhole welding.

show abstract

Sensing controlled pulse key-holing condition in plasma arc welding

Cited by 45 publications

References 5 publications

Adaptive Control by Convolutional Neural Network in Plasma Arc Welding System

Adaptive Control by Convolutional Neural Network in Plasma Arc Welding System

Prediction of Fusion Hole Perforation Based on Arc Characteristics of Front Image in Backing Welding

A Primary Study of Variable Polarity Plasma Arc Welding Using a Pulsed Plasma Gas

Contact Info

Product

Resources

About