Classification of molten pool modes in variable polarity plasma arc welding based on acoustic signature

Saad, Emad; Wang, Huijun; Kovacevic, Radovan

doi:10.1016/j.jmatprotec.2005.03.020

Cited by 76 publications

(21 citation statements)

References 13 publications

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“…It is comparatively simple and easy to obtain important information from the audible arc sound (Lv et al, 2014;Sumesh et al, 2015). The arc sounds were detected for identifying the no-keyhole mode, transition mode and keyhole mode in plasma arc welding (Saad et al, 2006), which concluded that the information contained in sound signal can reflect the variation of keyhole conditions, and there existed a definite link between the sound signal and keyhole geometry. However, the acoustic signal cannot reflect the information of keyhole geometry directly.…”

Section: Introductionmentioning

confidence: 97%

A prediction model for keyhole geometry and acoustic signatures during variable polarity plasma arc welding based on extreme learning machine

Chen

et al. 2016

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Purpose The purpose of this paper is to investigate the relationship between the keyhole geometry and acoustic signatures from the backside of a workpiece. It lays a solid foundation for monitoring the penetration state in variable polarity keyhole plasma arc welding. Design/methodology/approach The experiment system is conducted on 6-mm-thick aluminum alloy plates based on a dual-sensor system including a sound sensor and a charge coupled device (CCD) camera. The first step is to extract the keyhole boundary from the acquired keyhole images based on median filtering and edge extraction. The second step is to process the acquired acoustic signal to obtain some typical time domain features. Finally, a prediction model based on the extreme learning machine (ELM) technique is built to recognize different keyhole geometries through the acoustic signatures and then identify the welding penetration status according to the recognition results. Findings The keyhole geometry and acoustic features after processing can be closely related to dynamic change information of keyhole. These acoustic features can predict the keyhole geometry accurately based on the ELM model. Meanwhile, the predict results also can identify different welding penetration status. Originality/value This paper tries to make a foundation work to achieve the monitoring of keyhole condition and penetration status through image and acoustic signals. A useful model, ELM, is built based on these features for predicting the keyhole geometry. Compared with back-propagating neural network and support vector machine, this proposed model is faster and has better generalization performance in the case studied in this paper.

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Section: Introductionmentioning

confidence: 97%

A prediction model for keyhole geometry and acoustic signatures during variable polarity plasma arc welding based on extreme learning machine

Chen

et al. 2016

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“…Welding is often accompanied by various physical phenomena [4,5], which contain numerous pieces of information closely related to the penetration state of the weld. Many sensing methods are adopted to obtain such information.…”

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

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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.

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“…In addition to directly observing the keyhole with a CCD camera, the unique signal characteristics of VPPA welding were used to measure the keyhole state. Saad et al [12] achieved identification between three models of VPPA keyhole welding (no-keyhole, keyhole and cutting) using acoustic signal measurement. Wu et al [13] also investigated the relationship between the keyhole geometry and acoustic signatures with a dual-sensor system.…”

Section: Introductionmentioning

confidence: 99%

Effect of Arc Pressure on the Digging Process in Variable Polarity Plasma Arc Welding of A5052P Aluminum Alloy

Tashiro

Jiang

et al. 2019

Materials

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The keyhole digging process associated with variable polarity plasma arc (VPPA) welding remains unclear, resulting in poor control of welding stability. The VPPA pressure directly determines the dynamics of the keyhole and weld pool in the digging process. Here, through a high speed camera, high frequency pulsed diode laser light source and X-ray transmission imaging system, we reveal the potential physical phenomenon of a keyhole weld pool. The keyhole depth changes periodically corresponding to the polarity conversion period if the current is same in the electrode negative (EN) phase and electrode positive (EP) phase. There exist three distinct regimes of keyhole and weld pool behavior in the whole digging process, due to the arc pressure attenuation and energy accumulation effect. The pressure in the EP phase is smaller than that of the EN phase, causing the fluctuation of the weld pool free surface. Based on the influence mechanism of energy and momentum transaction, the arc pressure output is balanced by separately adjusting the current in each polarity. Finally, the keyhole fluctuation during the digging process is successfully reduced and welding stability is well controlled.

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Classification of molten pool modes in variable polarity plasma arc welding based on acoustic signature

Cited by 76 publications

References 13 publications

A prediction model for keyhole geometry and acoustic signatures during variable polarity plasma arc welding based on extreme learning machine

A prediction model for keyhole geometry and acoustic signatures during variable polarity plasma arc welding based on extreme learning machine

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

Effect of Arc Pressure on the Digging Process in Variable Polarity Plasma Arc Welding of A5052P Aluminum Alloy

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