scholarly journals Optimization of Enhanced TIG Welding Process Using Artificial Neural Network and Heuristic Algorithms

2021 ◽  
Author(s):  
Masoud Azadi Moghaddam ◽  
Farhad Kolahan
Keyword(s):  
Neural Network ◽  
Aspect Ratio ◽  
Heuristic Algorithms ◽  
Welding Process ◽  
Welding Current ◽  
Weld Bead ◽  
Depth Of Penetration ◽  
Bead Width ◽  
Modeling And Optimization ◽  
Gtaw Process

Abstract Using conventional gas tungsten arc welding (C-GTAW) process includes some demerits, shallow penetration has been considered as the most important ones. Recently, in order to cope with the mentioned disadvantage (low penetration), using a paste like coating of activating flux during welding process known as activated GTAW (AGTAW) has been proposed. In this paper, effect of A-GTAW process input adjusting parameters including welding speed (S), welding current (C) and percentage of activating fluxes (TiO2 and SiO2) combination (F) on weld bead width (WBW), depth of penetration (DOP), and consequently aspect ratio (ASR) (the most important quality characteristics) in welding of AISI316L parts have been studied. Box-behnken design (BBD) of experiments has been used to prepare the required experimental matrix for modeling and optimization objectives. Back propagation neural network (BPNN), architecture (hidden layers number and their corresponding neurons/nodes) of which has been determined using heuristic algorithm employed to model the process outputs, the most fitted ones have been optimized using simulated annealing (SA), and particle swarm optimization (PSO) algorithms in order to obtain the desired aspect ratio, maximum depth of penetration, and minimum weld bead width. Finally, confirmation experimental tests have been carried out to evaluate the performance of the proposed method. Due to the obtained results, the suggested method for modeling and optimization of A-GTAW process is quite efficient (with less than 4% error).

scholarly journals Activated Gas Tungsten Arc Welding Process Optimization Using Artificial Neural Network and Heuristic Algorithms

2021 ◽  
Author(s):  
Masoud Azadi Moghaddam ◽  
Farhad Kolahan
Keyword(s):  
Neural Network ◽  
Aspect Ratio ◽  
Arc Welding ◽  
Welding Process ◽  
Gas Tungsten Arc Welding ◽  
Depth Of Penetration ◽  
Bead Width ◽  
Modeling And Optimization ◽  
Gas Tungsten Arc ◽  
Gtaw Process

Abstract Apart from different merits of using conventional gas tungsten arc welding (C-GTAW) process, shallow penetration has been considered as the most important drawback of the process. Recently, in order to cope with the low penetration, using a paste like coating of activating flux during welding process known as activated GTAW (A-GTAW) has been proposed. In this paper, effect of A-GTAW process input parameters (welding speed (S), welding current (C)) and percentage of activating fluxes (TiO2 and SiO2) combination (F)) on the most important quality characteristics (weld bead width (WBW), depth of penetration (DOP), and consequently aspect ratio (ASR)) for AISI316L parts have been considered. The data needed for the modeling and optimization objectives, box-behnken design (BBD) of experiments, back propagation neural network (BPNN), simulated annealing (SA), and particle swarm optimization (PSO) algorithms have been employed. Moreover, PSO algorithm has been used to determine the proper ANN architecture (hidden layers number and their corresponding neurons/nodes) and optimize the proper ANN model to obtain the desired aspect ratio, maximum depth of penetration, and minimum weld bead width. Next, SA algorithm has been used to avoid getting trapped in local minima. Finally, confirmation experimental tests have been carried out to evaluate the performance of the proposed method. Due to the obtained results, the suggested method for modeling and optimization of A-GTAW process is quite efficient (with less than 4% error).

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.

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.

Overlay Welding on Titanium and 304 Stainless Steel Using ERNiCu-7 Filler Metal by GTAW Process

2017 ◽  
Vol 728 ◽  
pp. 60-65
Author(s):  
Thanaporn Thonondaeng ◽  
Ghit Laungsopapun ◽  
Kittichai Fakpan ◽  
Krittee Eidhed
Keyword(s):  
Stainless Steel ◽  
Base Metal ◽  
Filler Metal ◽  
Welding Current ◽  
304 Stainless Steel ◽  
Depth Of Penetration ◽  
Bead Width ◽  
Gtaw Process ◽  
Overlay Welding ◽  
Cp Ti

Single pass overlay welding of the ERNiCu-7 filler metal on the commercial pure titanium grade 2 and the 304 stainless steel using the gas tungsten arc welding (GTAW) process was studied. The ERNiCu-7 filler metal was overlay welded on the base metals with varying welding currents; it was 30A, 40A and 50A for the CP-Ti base metal and 50A, 60A and 70A for the 304SS base metal. The experimental results showed that the overlay CP-Ti welded-specimen, increasing of welding current increased bead width and decreased depth of penetration of weldment. While for the 304SS welded-specimen, increasing of welding current increased both bead width and depth of penetration. Suitable heat inputs to achieve good geometry of weldment for overlay welding were 348J/mm for CP-Ti welded-specimen and 558J/mm for 304SS welded-specimen.

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.

Multi Objective Optimization of Laser Welding Process Parameters by Desirability Approach of Design of Experiments

2012 ◽  
Author(s):  
T. Suthakar ◽  
K. R. Balasubramanian ◽  
K. Sankaranarayanasamy
Keyword(s):  
Aspect Ratio ◽  
Laser Welding ◽  
Power Density ◽  
Process Parameters ◽  
Welding Process ◽  
Depth Of Penetration ◽  
Bead Width ◽  
Desirability Approach ◽  
Laser Welding Process ◽  
Selection Of

Laser welding process is the high energy beam welding process which is very much used for thin and thick section industrial applications. The weld bead profile relies on the selection of process parameter. Due to its high power density optimal selection of process parameters is vital. In this research the optimization of the input process parameters namely power density (PD), welding speed (WS), beam angle (BA) and gas flow rate (GFR) on the response bead width (BW), depth of penetration (DOP) and depth to width aspect ratio (D/W) is analyzed. As the process parameters are highly non-linear, quadratic equations are generated for determining the desired response. The experimental trials are performed on an AISI 304 austenitic stainless steel using the four-factor-five-level central composite experimental design (CCED). Optimization of process parameters is performed using the desirability approach and the results obtained from the mathematical model is compared with the experimental results and found to be in agreement. The target fixed for the weld is to determine the optimal process parameters for the minimization of bead width and the maximization of depth of penetration and depth to width aspect ratio.

An integrated evolutionary approach for simultaneous optimization of laser weld bead characteristics

2016 ◽  
Vol 232 (8) ◽  
pp. 1407-1422 ◽  
Author(s):  
C. Govinda Rajulu ◽  
A Gopala Krishna ◽  
Thella Babu Rao
Keyword(s):  
Heat Affected Zone ◽  
Welding Process ◽  
Weld Bead ◽  
Welding Parameters ◽  
Process Conditions ◽  
Multi Objective Optimization ◽  
Depth Of Penetration ◽  
Control Variables ◽  
Bead Width ◽  
Multi Objective

The selection of optimal welding parameters in any welding process significantly improves the quality, production rate, and cost of a component. The weld bead characteristics such as bead width, depth of penetration, and heat-affected zone are the prominent factors for evaluating the performance of a welded joint. The work presents a novel evolutionary multi-objective optimization approach to derive the optimal laser welding conditions for the weld bead geometrical parameters. The welding experiments were conducted with the consideration of pulse frequency, pulse width, welding speed, and pulse energy as the process-control variables to evaluate the weld bead characteristics. Empirical models for the bead characteristics were developed in terms of the input variables using response surface methodology. The individual and interactive effects of the variables on the responses were also analyzed. As the influence of control variables on the bead characteristics is conflicting in nature, the problem is formulated as a multi-objective optimization problem to simultaneously optimize the output parameters. The aim is to simultaneously minimize the bead width, maximize the depth of penetration, and minimize the heat-affected zone. An efficient evolutionary algorithm called non-dominated sorting genetic algorithm-II was applied to derive the set of Pareto-optimal solutions. The derived optimal process responses were confirmed with the experimental values. The proposed integrated methodology can be applied to any welding process to automate the process conditions in computer-integrated manufacturing environment.

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.

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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