scholarly journals The Effect of Polarity and Hydrostatic Pressure on Operational Characteristics of Rutile Electrode in Underwater Welding

Materials ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5001 ◽  
Author(s):  
Andrés M. Moreno-Uribe ◽  
Alexandre Q. Bracarense ◽  
Ezequiel C. P. Pessoa

In order to provide a better understanding of the phenomena that define the weld bead penetration and melting rate of consumables in underwater welding, welds were developed with a rutile electrode in air welding conditions and at the simulated depths of 5 and 10 m with the use of a hyperbaric chamber and a gravity feeding system. In this way, voltage and current signals were acquired. Data processing involved the welding voltage, determination of the sum of the anodic and cathodic drops, calculation of the short-circuit factor, and determination of the melting rate. Cross-sectional samples were also taken from the weld bead to assess bead geometry. As a result, the collected data show that the generation of energy in the arc–electrode connection in direct polarity (direct current electrode negative-DCEN) is affected by the hydrostatic pressure, causing a loss of fusion efficiency, a drop of operating voltage, decreased arc length, and increased number of short-circuit events. The combination of these characteristics kept the weld bead geometry unchanged, compared to dry weld conditions. With the positive electrode (direct current electrode positive-DCEP), radial losses were derived from greater arc lengths resulting from increasing hydrostatic pressure, which led to a decrease in weld penetration.

2015 ◽  
Vol 11 (4) ◽  
pp. 494-506 ◽  
Author(s):  
Ravinder Pal Singh ◽  
R. K. Garg ◽  
D. K. Shukla

Purpose – Optimization of response parameter is essential in any process .The purpose of this paper is to focus at achieving the optimized parameter for submerged arc welding to furnish the quality welds at direct current electrode positive (DCEP) polarity and direct current electrode negative (DCEN) polarity. Design/methodology/approach – This paper achieves the parameter after extensive trial runs and finally parameters are optimized to acquire the cost effective and quality welds in submerged arc welding using the response surface methodology. Findings – Apart from effect of parameters on weld bead geometry has been identified but optimized parameters has also been achieved for submerged arc welding process for DCEP and DCEN polarities. Practical implications – As this study is related to practical work it may be useful for any relevant application. Social implications – The process parameters used in this experimental work will be basis for job work/industry for submerged arc welding. Originality/value – This paper identifies the effect of polarity in submerged arc welding.


2017 ◽  
Vol 26 (102) ◽  
pp. 110-119
Author(s):  
D. S. Yarymbash, ◽  
◽  
S. T. Yarymbash, ◽  
T. E. Divchuk, ◽  
D. A. Litvinov

SIMULATION ◽  
2021 ◽  
pp. 003754972110315
Author(s):  
B Girinath ◽  
N Siva Shanmugam

The present study deals with the extended version of our previous research work. In this article, for predicting the entire weld bead geometry and engineering stress–strain curve of the cold metal transfer (CMT) weldment, a MATLAB based application window (second version) is developed with certain modifications. In the first version, for predicting the entire weld bead geometry, apart from weld bead characteristics, x and y coordinates (24 from each) of the extracted points are considered. Finally, in the first version, 53 output values (five for weld bead characteristics and 48 for x and y coordinates) are predicted using both multiple regression analysis (MRA) and adaptive neuro fuzzy inference system (ANFIS) technique to get an idea related to the complete weld bead geometry without performing the actual welding process. The obtained weld bead shapes using both the techniques are compared with the experimentally obtained bead shapes. Based on the results obtained from the first version and the knowledge acquired from literature, the complete shape of weld bead obtained using ANFIS is in good agreement with the experimentally obtained weld bead shape. This motivated us to adopt a hybrid technique known as ANFIS (combined artificial neural network and fuzzy features) alone in this paper for predicting the weld bead shape and engineering stress–strain curve of the welded joint. In the present study, an attempt is made to evaluate the accuracy of the prediction when the number of trials is reduced to half and increasing the number of data points from the macrograph to twice. Complete weld bead geometry and the engineering stress–strain curves were predicted against the input welding parameters (welding current and welding speed), fed by the user in the MATLAB application window. Finally, the entire weld bead geometries were predicted by both the first and the second version are compared and validated with the experimentally obtained weld bead shapes. The similar procedure was followed for predicting the engineering stress–strain curve to compare with experimental outcomes.


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