Performance of weld bead profile during A-TIG welding on nitrogen alloyed stainless steel

2021 ◽  
Author(s):  
Akash Deep ◽  
Vivek Singh ◽  
Som Ashutosh ◽  
M. Chandrasekaran ◽  
Dixit Patel
Keyword(s):  
Stainless Steel ◽  
Welding Process ◽  
Weld Bead ◽  
Tig Welding ◽  
Inert Gas ◽  
Bead Geometry ◽  
Depth Of Penetration ◽  
Weld Bead Geometry ◽  
A Tig Welding ◽  
Arc Welding Process

Abstract Austenitic stainless steel (ASS) is widely fabricated by tungsten inert gas (TIG) welding for aesthetic look and superior mechanical properties while compared to other arc welding process. Hitherto, the limitation of this process is low depth of penetration and less productivity. To overcome this problem activated tungsten inert gas (A-TIG) welding process is employed as an alternative. In this investigation the welding performance of conventional TIG welding is compared with A-TIG process using TiO2 and SiO2 flux with respect to weld bead geometry. The experimental investigation on A-TIG welding of ASS-201 grade shows TiO2 flux helps in achieve higher penetration as compared to SiO2 flux. While welding with SiO2 the hardness in HAZ and weld region higher than that of TIG welding process.

scholarly journals Experimental and Numerical Analysis on TIG Arc Welding of Stainless Steel Using RSM Approach

Metals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1659
Author(s):  
Sasan Sattarpanah Karganroudi ◽  
Mahmoud Moradi ◽  
Milad Aghaee Attar ◽  
Seyed Alireza Rasouli ◽  
Majid Ghoreishi ◽  
...  
Keyword(s):  
Heat Flux ◽  
Stainless Steel ◽  
Welding Speed ◽  
Arc Welding ◽  
Welding Process ◽  
Weld Bead ◽  
Bead Geometry ◽  
Depth Of Penetration ◽  
Weld Bead Geometry ◽  
Bead Width

This study involves the validating of thermal analysis during TIG Arc welding of 1.4418 steel using finite element analyses (FEA) with experimental approaches. 3D heat transfer simulation of 1.4418 stainless steel TIG arc welding is implemented using ABAQUS software (6.14, ABAQUS Inc., Johnston, RI, USA), based on non-uniform Goldak’s Gaussian heat flux distribution, using additional DFLUX subroutine written in the FORTRAN (Formula Translation). The influences of the arc current and welding speed on the heat flux density, weld bead geometry, and temperature distribution at the transverse direction are analyzed by response surface methodology (RSM). Validating numerical simulation with experimental dimensions of weld bead geometry consists of width and depth of penetration with an average of 10% deviation has been performed. Results reveal that the suggested numerical model would be appropriate for the TIG arc welding process. According to the results, as the welding speed increases, the residence time of arc shortens correspondingly, bead width and depth of penetration decrease subsequently, whilst simultaneously, the current has the reverse effect. Finally, multi-objective optimization of the process is applied by Derringer’s desirability technique to achieve the proper weld. The optimum condition is obtained with 2.7 mm/s scanning speed and 120 A current to achieve full penetration weld with minimum fusion zone (FZ) and heat-affected zone (HAZ) width.

Parametric Optimization of TIG Welding on 316L Austenitic Stainless Steel by Grey-Based Taguchi Method

2011 ◽  
Vol 383-390 ◽  
pp. 4667-4671 ◽  
Author(s):  
Nanda Naik Korra ◽  
K.R. Balasubramanian
Keyword(s):  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Gas Flow ◽  
Welding Process ◽  
Weld Bead ◽  
Bead Geometry ◽  
Depth Of Penetration ◽  
Weld Bead Geometry ◽  
Bead Width ◽  
Input Parameters

Gas Tungsten Arc Welding (GTAW) is one of the most widely used welding process in industry. The input parameters play a very significant role in determining the quality of a welded joint (geometry of weld bead). The joint quality can be evaluated by studying the features of weld bead geometry (output parameters) such as Bead Width (BW), Bead Height (BH) and Depth of Penetration (DP). Present study focused on welding of austenitic stainless steel sheets using GTAW process with 316L material. The output variables are determined according to gas flow rate, travel speed and current. Grey relational analysis is applied to optimize the input parameters simultaneously considering the multiple output variables. Finally, confirmation experiment has been conducted to validate the optimized parameters and found to be correlated.

Development of Mathematical Models for Prediction of Weld Bead Geometry of GTAW Stainless Steel

2017 ◽  
Vol 867 ◽  
pp. 88-96
Author(s):  
S.M. Ravikumar ◽  
P. Vijian
Keyword(s):  
Stainless Steel ◽  
Process Parameters ◽  
Welding Current ◽  
Weld Bead ◽  
304 Stainless Steel ◽  
Graphical Form ◽  
Bead Geometry ◽  
Depth Of Penetration ◽  
Weld Bead Geometry ◽  
Bead Width

Welding input process parameters are playing a very significant role in determining the weld bead quality. The quality of the joint can be defined in terms of properties such as weld bead geometry, mechanical properties and distortion. Experiments were conducted to develop models, using a three factor, five level factorial design for 304 stainless steel as base plate with ER 308L filler wire of 1.6 mm diameter. The purpose of this study is to develop the mathematical model and compare the observed output values with predicted output values. Welding current, welding speed and nozzle to plate distance were chosen as input parameters, while depth of penetration, weld bead width, reinforcement and dilution as output parameters. The models developed have been checked for their adequacy. Confirmation experiments were also conducted and the results show that the models developed can predict the bead geometries and dilution with reasonable accuracy. The direct and interaction effect of the process parameters on bead geometry are presented in graphical form.

A Study of Software Approach for Predicting Weld Bead Geometry in Shielded Metal Arc Welding (SMAW) Process

2014 ◽  
Vol 554 ◽  
pp. 386-390
Author(s):  
C.W. Mohd Noor ◽  
Manuhutu Ferry ◽  
W.B. Wan Nik
Keyword(s):  
Arc Welding ◽  
Welding Process ◽  
Weld Bead ◽  
Bead Geometry ◽  
Shielded Metal Arc Welding ◽  
Depth Of Penetration ◽  
Weld Bead Geometry ◽  
Bead Width ◽  
Real Process ◽  
Metal Arc Welding

The prediction of the optimal weld bead width is an important aspect in shielded metal arc welding (SMAW) process as it is related to the strength of the weld. This paper focuses on investigation of the development of the simple and accurate model for prediction of weld bead geometry. The experiment used welding current, arc length, welding speed, welding gap and electrode diameter as input parameters. While output parameters are bead width, depth of penetration and weld reinforcement. A number of 33 mild steel plate specimens had undergone the SMAW welding process. The experimental data was used to develop mathematical models using SPSS software. The actual and predicted values of the weld bead geometry are compared. The proposed models shows positive correlation to the real process.

Prediction of Weld Bead Geometry in Chromium-Manganese Stainless Steel Gas Tungsten Arc Welded Plates Using Artificial Neural Networks

2013 ◽  
Vol 3 (3) ◽  
pp. 13-41 ◽  
Author(s):  
R. Sudhakaran ◽  
P. S. Siva Sakthivel
Keyword(s):  
Stainless Steel ◽  
Process Parameters ◽  
Weld Bead ◽  
Fractional Factorial ◽  
Welding Parameters ◽  
Width Ratio ◽  
Bead Geometry ◽  
Depth Of Penetration ◽  
Weld Bead Geometry ◽  
Bead Width

The quality of the weld joint is highly influenced by the welding parameters. Hence accurate prediction of weld bead parameters is highly essential to achieve good quality joint. This paper presents development of neural network models for predicting bead parameters such as depth of penetration, bead width and depth to width ratio for AISI 202 grade stainless steel GTAW plates. The use of this series in certain applications ended in failure of the product as there is no adequate level of user knowledge. Hence it becomes imperative to go for detailed investigations on this grade before recommending it for any application. The process parameters chosen for the study are welding current, welding speed, gas flow rate and welding gun angle. The chosen output parameters were depth of penetration, bead width and depth to width ratio. The experiments were conducted based on design of experiments using fractional factorial with 125 runs. Using the experimental data feed forward back propagation neural net work models were developed and trained using Levenberg Marquardt algorithm. The training, learning, performance and transfer functions used are trainlm, learningdm, MSE and tansig respectively. Four networks were developed with four neurons for the input layer, 3 neurons for the output layer and different nodes for the hidden layer. They are 4 – 2 – 3, 4 – 4 – 3, 4 – 8 – 3 and 4 – 9 – 3. It was found that ANN model based on network 4 – 9 – 3 predicted the bead dimensions more accurately than the other networks. The prediction of weld bead geometry parameters helps in identifying the recommended combination of process parameters to achieve good quality joint.

Mathematical Expression for the Prediction of Penetration in Butt Joints for Duplex Stainless Steel SAF-2205

2003 ◽  
Author(s):  
Mari´a Carolina Payares ◽  
Minerva Dorta Almenara
Keyword(s):  
Stainless Steel ◽  
Arc Welding ◽  
Welding Process ◽  
Weld Bead ◽  
Welding Parameters ◽  
Linear Regression Method ◽  
Bead Geometry ◽  
Butt Joints ◽  
Weld Bead Geometry ◽  
Saf 2205

In order to understand the mechanism of weld bead formation, a relationship between arc welding parameters and weld bead geometry must be established. This relationship is also necessary to forecast penetration variables allowing to optimize welding parameters for particular applications. Specifically in duplex stainles steel SAF-2205 welding, the influence of arc current, arc voltage and welding speed on the penetration have been empirically studied. In this research, using a multiple linear regression method, the statistical analyses produced twelve (12) potential function dependent of these welding parameters that determines the weld bead geometry in butt joints of DSS SAF 2205 using GAs Metal Arc Welding process. the mathematical model gave as a result, a very approximate contour of the weld bead geometry between the established ranges of welding parameters used. Also, the influence of these variables on the weld panetration is studied, providing with new evidence in stainless steel welding.

Optimization of A-TIG Welding Process Using Simulated Annealing Algorithm

2020 ◽  
Vol 19 (04) ◽  
pp. 869-891
Author(s):  
Masoud Azadi Moghaddam ◽  
Farhad Kolahan
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Welding Process ◽  
Welding Current ◽  
Tig Welding ◽  
Inert Gas ◽  
Regression Equations ◽  
Depth Of Penetration ◽  
Bead Width ◽  
A Tig Welding

Flux-assisted tungsten inert gas welding process, also known as activated tungsten inert gas (A-TIG) welding, is extensively used in order to improve the performance of the conventional TIG welding process. In this study, the orthogonal array Taguchi (OA-Taguchi) method, regression modeling, analysis of variance (ANOVA) and simulated annealing (SA) algorithm have been used to model and optimize the process responses in A-TIG welding process. Welding current (I), welding speed (S) and welding gap (G) have been considered as process input variables for fabricating AISI316L austenitic stainless steel specimens. Depth of penetration (DOP) and weld bead width (WBW) have been taken into account as the process responses. In this study, SiO2, nano-particle has been considered as an activating flux. To gather required data for modeling, statistical analysis and optimization purposes, OA-Taguchi based on the design of experiments (DOE) has been employed. Then the process responses have been measured and their corresponding signal-to-noise (S/N) ratio values have been calculated. Different regression equations have been applied to model the responses. Based on the ANOVA results, the most fitted models have been selected as an authentic representative of the process responses. Furthermore, the welding current has been determined as the most important variable affecting DOP and WBW with 68% and 88% contributions, respectively. Next, the SA algorithm has been used to optimize the developed models in such a way that WBW is minimized and DOP is maximized. Finally, experimental performance evaluation tests have been carried out, based on which it can be concluded that the proposed procedure is quite efficient (with less than 4% error) in modeling and optimization of the A-TIG welding process.

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