Diffusible Hydrogen Control in Flux Cored Arc Welding Process

2013 ◽  
Vol 597 ◽  
pp. 171-178 ◽  
Author(s):  
Dariusz Fydrych ◽  
Aleksandra Świerczyńska ◽  
Jacek Tomków
Keyword(s):  
Welding Process ◽  
Welding Current ◽  
Welding Parameters ◽  
Hydrogen Degradation ◽  
Diffusible Hydrogen ◽  
Cored Wire ◽  
Flux Cored Arc Welding ◽  
Deposited Metal ◽  
Relevant Variables ◽  
Arc Welding Process

One of the types of hydrogen degradation of steel welded joints is cold cracking. The direct cause of the formation of cold cracks is simultaneous presence of hydrogen, residual stresses and brittle structure. The way of preventing the occurring of degradation is to eliminate at least one of these factors. Practice has shown that the best solution is to control the amount of hydrogen in deposited metal. In this paper an experimental evaluation of the effect of the welding parameters on the content of diffusible hydrogen in deposited metal obtained from rutile flux cored wire grade H10 was carried out. The state of the art of considered issues was described and results of preliminary investigations were presented. Five factors were considered: the flow rate of shielding gas, the welding current, the arc voltage, the welding speed and the electrode extension. All factors were optimized using a Plackett-Burman design to get the most relevant variables. The level of diffusible hydrogen was determined by a glycerin test. The results of the experiment indicate that appropriate choice of welding parameters may significantly reduce diffusible hydrogen content in deposited metal.

scholarly journals Real-Time Weld Gap Monitoring and Quality Control Algorithm during Weaving Flux-Cored Arc Welding Using Deep Learning

Metals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1135
Author(s):  
Chengnan Jin ◽  
Sehun Rhee
Keyword(s):  
Arc Welding ◽  
Control Algorithm ◽  
Weld Seam ◽  
Welding Process ◽  
Welding Current ◽  
Forming Process ◽  
Flux Cored Arc Welding ◽  
Arc Welding Process ◽  
Shell Forming ◽  
The Time Domain

In the flux-cored arc welding process, which is most widely used in shipbuilding, a constant external weld bead shape is an important factor in determining proper weld quality; however, the size of the weld gap is generally not constant, owing to errors generated during the shell forming process; moreover, a constant external bead shape for the welding joint is difficult to obtain when the weld gap changes. Therefore, this paper presents a method for monitoring the weld gap and controlling the weld deposition rate based on a deep neural network (DNN) for the automation of the hull block welding process. Welding experiments were performed with a welding robot synchronized with the welding machine, and the welding quality was classified according to the experimental results. Welding current and voltage signals, as the robot passed through the weld seam, were measured using a trigger device and analyzed in the time domain and frequency domain, respectively. From the analyzed data, 24 feature variables were extracted and used as input for the proposed DNN model. Consequently, the offline and online performance verification results for new experimental data using the proposed DNN model were 93% and 85%, respectively.

The Effect of Welding Conditions on Diffusible Hydrogen Content in Deposited Metal

2011 ◽  
Vol 183 ◽  
pp. 193-200 ◽  
Author(s):  
Aleksandra Świerczyńska ◽  
Dariusz Fydrych ◽  
Jerzy Łabanowski
Keyword(s):  
Welded Joint ◽  
Welding Current ◽  
Cold Cracking ◽  
Welding Parameters ◽  
Filler Materials ◽  
Hydrogen Atoms ◽  
Diffusible Hydrogen ◽  
Electrode Coating ◽  
Deposited Metal ◽  
Filler Metals

The primary limitation of weldability of high strength low alloy steel is susceptibility to cold cracking. The important reason of the formation of cold cracks, besides forming brittle structure and residual stresses, is the presence of diffusible hydrogen in welded joint. The most effective methods reducing the susceptibility to cold cracking are connected with decreasing the amount of potential hydrogen. This process may be carried out in technological (drying welding filler materials, preheating components) or in a metallurgical way (filler metals with austenitic structure, adding rare-earths elements to filler metals as traps for hydrogen atoms in steel). The possibility to minimize the amount of diffusible hydrogen by changing the welding parameters seems to be particularly interesting. The article presents the results of a literature survey and preliminary tests which set out effects of welding conditions on the amount of diffusible hydrogen in deposited metal. Experiments were conducted by using rutile coated electrodes which generate high contents of diffusible hydrogen in deposited metal. The amount of diffusible hydrogen was determined by a glycerin test. Eleven factors were considered: the electrode angle, the grinding of sample, the preheat temperature, the polarity of welding current, the welding current, the welding – cooling time, the electrode usage, the time of welding, the thickness of specimen, the welding – measurement time and thickness of electrode coating. All factors were optimized using a Plackett-Burman design to get the most relevant variables. The results of the preliminary tests indicate that appropriate choice of welding parameters may considerably reduce diffusible hydrogen in deposited metal. However, the range of parameters is limited by the necessity of providing stability of the welding process and obtaining required properties of the welded joint.

Modelling and analysis of delta ferrite content in claddings deposited by flux cored arc welding using a neural network

2009 ◽  
Vol 223 (4) ◽  
pp. 779-787 ◽  
Author(s):  
P K Palani ◽  
N Murugan
Keyword(s):  
Neural Network ◽  
Arc Welding ◽  
Welding Process ◽  
Welding Current ◽  
Ferrite Content ◽  
Delta Ferrite ◽  
Novel Approach ◽  
Flux Cored Arc Welding ◽  
Minimum Number ◽  
Arc Welding Process

Measurement of delta ferrite in cladding gives important insight into the future mechanical and corrosion resistant behaviour of the cladded structures. The amount of delta ferrite formed during cladding is influenced by process parameters such as welding speed, welding current, and nozzle-to-plate distance. Therefore, it is essential to predict the effect of these parameters on the formation of delta ferrite. This article discusses the development of an artificial neural network model to predict the delta ferrite content in austenitic stainless-steel claddings deposited by the flux cored arc welding process. A novel approach of using the design of experiments to collect data to train the network has been adopted in this investigation. The study revealed that the delta ferrite content can be predicted more accurately using the neural networks with a minimum number of experiments. The results also indicated that welding current and speed have a significant influence on the amount of ferrite and the interaction effects of these parameters play a major role in determining ferrite in the claddings.

On-Line Empirical Models for Control of Bead Geometry in Robotic Arc Welding Process

2011 ◽  
Vol 264-265 ◽  
pp. 367-372
Author(s):  
Joon Sik Son ◽  
Il Soo Kim ◽  
H.H. Kim ◽  
H.H. Na ◽  
J.H. Lee
Keyword(s):  
Gas Flow ◽  
Welding Process ◽  
Welding Current ◽  
Empirical Models ◽  
Seam Tracking ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Time Data ◽  
On Line ◽  
Arc Welding Process

Recently, not only robotic welders have replaced human welders in many welding applications, but also reasonable seam tracking systems are commercially available. However, fully adequate process control systems have not been developed due to a lack of reliable sensors and mathematical models that correlate welding parameters to the bead geometry for the automated welding process. Especially, real-time quality control in automated welding process is an important factor contributing to higher productivity, lower costs and greater reliability of the bead geometry. In this paper, on-line empirical models with experimental results are proposed in order to be applicable for the prediction of bead geometry. For development of the proposed predicting model, an attempt has been made to apply for a several methods. For the more accurate prediction, the prediction variables are first used to the surface temperatures measured using infrared thermometers with the welding parameters (welding current, arc voltage, CTWD and gas flow rate) because the surface temperature are strongly related to the formation of the bead geometry. And the developed model has been carried out a learning each time data acquired.

A Study on Experimental Design Methods for the Automatic GMA Welding Process

2019 ◽  
Vol 294 ◽  
pp. 119-123
Author(s):  
Zong Liang Liang ◽  
Tae Jong Yun ◽  
Won Bin Oh ◽  
Bo Ram Lee ◽  
Ill Soo Kim
Keyword(s):  
Experimental Design ◽  
Taguchi Method ◽  
Arc Welding ◽  
Gma Welding ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Welding Current ◽  
Welding Parameters ◽  
Bead Width ◽  
Arc Welding Process

Generally, the welding parameters directly affect the weld forming and the joint performance. Because many parameters are involved in the automatic arc welding process, it is not realistic to use traditional experimental methods, such as full factorial design. Therefore, it is important to find out the good experimental design method to determine the welding parameters for optimal joint quality with a minimal number of experiments. Therefore, this study is aimed at investigating the effect of DOE (Design of Experiment) methods on bead width of mild steel parts welded by the automatic GMA (Gas Metal Arc) welding process. In this work, Taguchi method was used for studying the effect of the welding parameters on optimization of bead width, while Box-Behnken method was utilized to develop a mathematical model relating the bead width to welding parameters such as welding voltage, arc current, welding speed and CTWD (Contact Tip to Work Distance). The S/N (Signal-to-Noise) ratio and the ANOVA (Analysis of Variance) were employed to find the optimal bead width. Confirmation tests were carried out to validate the effectiveness of the Taguchi method. The experimental results show that welding current mainly affected the bead width. The predicted bead width of 3.12mm was in good agreement with the confirmation tests. With the regression coefficient analysis in the Box-Behnken design, a relationship between bead width and four significant welding parameters was obtained. A second-order model has also been established between the welding parameters and the bead width as welding quality. The developed model is adequate to navigate the design space.

scholarly journals Influence of the formulation of a flux-cored wire on the microstructure and hardness of welded metal

2021 ◽  
Vol 2118 (1) ◽  
pp. 012010
Author(s):  
E C Amaral ◽  
J L Jácome-Carrascal ◽  
A M Moreno-Uribe ◽  
A Q Bracarense
Keyword(s):  
Weld Metal ◽  
Arc Welding ◽  
Welding Process ◽  
The Other ◽  
Volumetric Fraction ◽  
Cored Wire ◽  
Flux Cored Arc Welding ◽  
Arc Welding Process ◽  
Hardness Values ◽  
Robotics Welding

Abstract For this paper, the microstructure and hardness of the weld metal were investigated by conducting experiments with the flux cored arc welding process in underwater and air conditions. A rutile/oxidizing tubular wire was used, manufactured by the Robotics, Welding and Simulation Laboratory at Minas Gerais Federal University, especially for underwater wet welding. Underwater welds had a lower volumetric fraction of acicular ferrite in the weld metal compared to air welds. In the thermally affected zone, for both welds, there was a predominant formation of martensite. However, the grain size and width of the thermally affected zone of underwater welds are smaller. The hardness values shown correspond to the microstructure formed in the weld metal. On the other hand, in the region of the thermally affected zone, the hardness values were higher underwater welds, due to the smaller martensite grains presented.

A study on welding quality for the automatic vertical-position welding process based on Mahalanobis Distance method

2018 ◽  
Vol 1 (91) ◽  
pp. 31-40
Author(s):  
B.-J. Jin ◽  
M.-H. Park ◽  
T.-J. Yun ◽  
J.-Y. Shim ◽  
B.-Y. Kang ◽  
...  
Keyword(s):  
Mahalanobis Distance ◽  
Welding Process ◽  
Welding Current ◽  
Welding Parameters ◽  
Vertical Position ◽  
Welding Quality ◽  
Distance Method ◽  
Arc Voltage ◽  
On Line ◽  
Arc Welding Process

Purpose: The welding quality and reducing production cost could be achieved by developing the automatic on-line welding quality monitoring system. However, investigation of welding fault to quantify the welding quality on the horizontal-position welding has been concentrated. Therefore, MD (Mahalanobis Distance) method on the vertical-position welding process by analysing the transform arc voltage and welding current gained from the on-line monitoring system has been applied. Design/methodology/approach: The transformed welding current and arc voltage data were taken from the experiment whereby the data number was 2500 data/s. The prediction of Contact Tip to Work Distance (CTWD) to gain best welding quality using the waveform variations were then taken from the experimental results. MD was employed to quantify the welding quality by analysing the transformed arc voltage and welding current. Finally, the optimal CTWD setting has verified the developed algorithms through additional experiments. Two kinds of experiments has been carried out by changing welding parameters artificially to verify the sensitivity and feasibility of WQ (Welding Quality) based on the concepts of MD and normal distribution. Findings: The results represented that WQ was fully capable of quantifying and qualifying the welding faults for automatic vertical-position welding process. Research limitations/implications: The arc welding process on the vertical-position compared to a horizontal-position welding is much more difficult because the metal transfer is influenced by the gravity force. To solve the problem, a new algorithm to monitor and control the welding fault during the arc welding process has been developed. Furthermore, optimization of welding parameters for the vertical-position welding process was really difficult to use the developed algorithms because they are only useful in selecting stored data and not for evaluating the effect of the variation of welding parameters on the weld ability. Practical implications: The developed algorithm could be achieved the highest welding quality at 15mm CTWD setting which the welding quality is 99.50% for the start section and 99.68% at the middle section. Originality/value: This paper proposed a new algorithm which employed the concepts of MD (Mahalanobis Distance) and normal distribution to describe a good quality welding.

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