scholarly journals Impact of activated–flux tungsten inert gas (a-tig) welding on weld joint of a metal – Review

2019 ◽  
Vol 640 ◽  
pp. 012064
Author(s):  
S A Afolalu ◽  
S B Soetan ◽  
S O Ongbali ◽  
A A Abioye ◽  
A S Oni
Keyword(s):  
Weld Joint ◽  
Tig Welding ◽  
Inert Gas ◽  
A Tig Welding ◽  
Activated Flux

Effect of Manual and Automatic Activated Tungsten Inert Gas Welding Using Single Component Fluxes on Stainless Steel AISI-304

2020 ◽  
Vol 36 (01) ◽  
pp. 78-86
Author(s):  
Shaji Krishna Prasad ◽  
Andy Mathiazhagan ◽  
Pallichakkalayil Sasidharan Krishnadas
Keyword(s):  
Tig Welding ◽  
Inert Gas ◽  
Depth Of Penetration ◽  
Shipbuilding Industry ◽  
Aisi 304 ◽  
Iron And Steel ◽  
A Tig Welding ◽  
Steel Aisi ◽  
Carrier Solvent ◽  
Activated Flux

The study compared the effects of manual activated tungsten inert gas (A-TIG) welding and automated A-TIG welding on AISI (American Iron and Steel Institute)- 304 at three different values of current using commercially available powders of Al2O3, SiO2, Fe2O3, MgCl2, and TiO2 separately as activated flux and distilled water as carrier solvent. The effect of fluxes on the depth of penetration of the weldments, width of weldment, microstructure of the weldment, and microhardness of the weldment was investigated. Reverse Marangoni convection and arc constriction are found to be more effective in A-TIG manual welding, as aspect ratio obtained by A-TIG manual welding is greater as than that of automatic A-TIG welding. Microstructure of both the manual and automatic A-TIG-welded specimen is similar with no noticeable differences and almost same amount of intermetallic phases and carbon precipitates. Microhardness tests revealed that for Al2O3 and TiO2 fluxes, manual A-TIG-welded specimen have lower values of microhardness at weldment, heat-affected zone, and base metal than automated A-TIG-welded specimen. The aim of the study is to implement the manual A-TIG process in shipbuilding industry to improve the productivity of welding as automated A-TIG welding in the industry has limitations.

Design and Development of Knowledge Bases for Forward and Reverse Mappings of TIG Welding Process

2009 ◽  
pp. 185-200
Author(s):  
J. P. Ganjigatti ◽  
Dilip Kumar Pratihar
Keyword(s):  
Regression Analysis ◽  
Welding Process ◽  
Knowledge Bases ◽  
Tig Welding ◽  
Inert Gas ◽  
Statistical Regression ◽  
A Tig Welding ◽  
Input Variables ◽  
Forward Mapping ◽  
Reverse Mapping

In this chapter, an attempt has been made to design suitable knowledge bases (KBs) for carrying out forward and reverse mappings of a Tungsten inert gas (TIG) welding process. In forward mapping, the outputs (also known as the responses) are expressed as the functions of the input variables (also called the factors), whereas in reverse mapping, the factors are represented as the functions of the responses. Both the forward as well as reverse mappings are required to conduct, for an effective online control of a process. Conventional statistical regression analysis is able to carry out the forward mapping efficiently but it may not be always able to solve the problem of reverse mapping. It is a novel attempt to conduct the forward and reverse mappings of a TIG welding process using fuzzy logic (FL)-based approaches and these are found to solve the said problem efficiently.

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 Optimization of Activated Tungsten Inert Gas Welding Process Parameters Using Heat Transfer Search Algorithm: With Experimental Validation Using Case Studies

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 981
Author(s):  
Jay Vora ◽  
Vivek K. Patel ◽  
Seshasai Srinivasan ◽  
Rakesh Chaudhari ◽  
Danil Yurievich Pimenov ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Case Studies ◽  
Process Parameters ◽  
Optimization Technique ◽  
Carbon Steels ◽  
Weld Bead ◽  
Tig Welding ◽  
Inert Gas ◽  
Tungsten Inert Gas Welding ◽  
A Tig Welding

The Activated Tungsten Inert Gas welding (A-TIG) technique is characterized by its capability to impart enhanced penetration in single pass welding. Weld bead shape achieved by A-TIG welding has a major part in deciding the final quality of the weld. Various machining variables influence the weld bead shape and hence an optimum combination of machining variables is of utmost importance. The current study has reported the optimization of machining variables of A-TIG welding technique by integrating Response Surface Methodology (RSM) with an innovative Heat Transfer Search (HTS) optimization algorithm, particularly for attaining full penetration in 6 mm thick carbon steels. Welding current, length of the arc and torch travel speed were selected as input process parameters, whereas penetration depth, depth-to-width ratio, heat input and width of the heat-affected zone were considered as output variables for the investigations. Using the experimental data, statistical models were generated for the response characteristics. Four different case studies, simulating the real-time fabrication problem, were considered and the optimization was carried out using HTS. Validation tests were also carried out for these case studies and 3D surface plots were generated to confirm the effectiveness of the HTS algorithm. It was found that the HTS algorithm effectively optimized the process parameters and negligible errors were observed when predicted and experimental values compared. HTS algorithm is a parameter-less optimization technique and hence it is easy to implement with higher effectiveness.

Enhancement of activated tungsten inert gas (A-TIG) welding using multi-component TiO2-SiO2-Al2O3 hybrid flux

Measurement ◽  
2019 ◽  
Vol 148 ◽  
pp. 106912 ◽  
Author(s):  
Atul Babbar ◽  
Akhilesh Kumar ◽  
Vivek Jain ◽  
Dheeraj Gupta
Keyword(s):  
Tig Welding ◽  
Inert Gas ◽  
A Tig 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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