scholarly journals Modeling and Optimization of Weld Bead Profile with Varied Welding Stages for Weathering Steel A606

2021 ◽  
Author(s):  
Dawei Zhao ◽  
Yuriy Bezgans ◽  
Nikita Vdonin ◽  
Liudmila Radionova ◽  
Vitaly Bykov
Keyword(s):  
Desirability Function ◽  
Research Work ◽  
Welding Process ◽  
Weld Bead ◽  
Welding Parameters ◽  
Weathering Steel ◽  
Bead Geometry ◽  
Feed Speed ◽  
Geometric Features ◽  
Weld Bead Geometry

Abstract The profile of the welding bead changes with the welding process parameters during the gas metal arc welding (GMAW) process, the reinforcement disappears and the penetration becomes sunken when the excessive welding heat input is applied. However, little research work is specially planned to cope with the studying of welding bead at these stages. A systematic studying of the relationships among the welding process variables and welding bead geometric features and optimization of the welding quality is presented. The influences of the welding technological parameters (voltage, welding speed, and wire feed speed) on the welding geometry were revealed and the models correlating them were established. The features of the weld bead geometry were composed of top reinforcement width, top reinforcement height, penetration depth, bottom reinforcement width, and bottom reinforcement height. By the desirability function approach, the recommendation of suitable welding parameters to meet the contradicting demands of multiple bead geometric features is fulfilled. The microstructure in different welding regions and mechanical performances of the welding joints produced by the verification test were also studied.

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.

A modified version of MATLAB application window for predicting the weld bead profile and stress–strain plot of AA5052 CMT weldment using ER4043

SIMULATION ◽  
2021 ◽  
pp. 003754972110315
Author(s):  
B Girinath ◽  
N Siva Shanmugam
Keyword(s):  
Strain Curve ◽  
Weld Bead ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Hybrid Technique ◽  
Stress Strain Curve ◽  
Stress Strain ◽  
Weld Bead Geometry ◽  
Inference System ◽  
Engineering Stress

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.

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.

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 Flux Cored Arc Welding (FCAW) Parameters and Bead Geometry in Downhill Position (3F)

2014 ◽  
Vol 660 ◽  
pp. 342-346
Author(s):  
Nik Mohd Baihaki Abd Rahman ◽  
Abdul Ghalib Tham ◽  
Sunhaji Kiyai Abas ◽  
Razali Hassan ◽  
Yupiter H.P. Manurung ◽  
...  
Keyword(s):  
Arc Welding ◽  
Weld Bead ◽  
Welding Parameter ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Absolute Deviation ◽  
High Productivity ◽  
Flux Cored Arc Welding ◽  
Wide Range

The robot can perform Flux Cored Arc Welding (FCAW) at high productivity and consistency in quality. The quality of the welding depend on the selection of welding parameter and deposition geometry. These input has to be known before the start of production, generally the welding operator will obtain the information through experimental trial and error. This project planned to develop a tool that can advise the choice of welding parameter that produce quality weld bead with desired geometry. This research focused on the correlation of heat input on weld bead geometry and the range of welding parameter for fillet design welded in downhill direction (3F). From the correlation trend-line equations and welding parameter population boundary, the weld bead geometry and welding parameter for quality deposit are predicted. Consequently two calculators were developed to display the values digitally. The deviation of predicted bead geometry from actual welding is less than 1mm. Mean Absolute Deviation (MAD) is less than 0.4mm, accuracy is good. A wide range of welding parameters can be generated for quality welding at desired bead geometry.

Analysis of Weld Bead Parameters of Overlay Deposited on D2 Steel Components by Plasma Transferred Arc (PTA) Process

2013 ◽  
Vol 755 ◽  
pp. 39-45 ◽  
Author(s):  
F. García-Vázquez ◽  
A. Aguirre ◽  
Ana Arizmendi-Morquecho ◽  
H.M. Hernández-García ◽  
L. Santiago-Bautista ◽  
...  
Keyword(s):  
Tool Steel ◽  
Cold Work ◽  
Welding Process ◽  
Wear Test ◽  
Weld Bead ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Plasma Transferred Arc ◽  
Transferred Arc ◽  
D2 Steel

Plasma Transferred Arc (PTA) process is increasingly used in applications where enhancement of wear, corrosion and heat resistance of metals surface is required. The shape of weld bead geometry affected by the PTA welding process parameters is an indication of the quality of the weld. PTA is a versatile method of depositing high-quality metallurgically fused deposits on relatively low cost surfaces. The overlay deposited is an alloy that is hard and more corrosion resistant than counterparts laid down by Gas Tungsten Arc Welding (GTAW) or Oxy Fuel Welding (OFW) processes. Weld deposits are characterized by very low levels of inclusions, oxides, and discontinuities. This process produces smooth deposits that significantly reduce the amount of post weld machining required. Metal-Mechanic industry continuously requires recovering tool steel components subjected to severe wear. The steel known as D2 is considered to be a high carbon, high chromium cold work tool steel. In this research, weld beads were deposited on D2 steel by using PTA process with different parameters as welding current and travel speed using base nickel filler metal. In order to evaluate the metallurgical features on the weld beads/substrate interface a microstructural characterization was performed by using Scanning Electron Microscopy (SEM) and to evaluate the mechanical properties was conducted the wear test.

scholarly journals Prediction and Optimization of Weld Bead Geometry

2019 ◽  
Vol 8 (10) ◽  
pp. 2735-2742
Keyword(s):  
Weld Bead ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Bead Width ◽  
Current Voltage ◽  
Weld Parameters ◽  
Curvilinear Regression ◽  
Travel Rate

Bead geometry plays very important role in predicting the quality of weld as cooling rate of the weld depends on the height and bead width, also bead geometry determines it’s residual stresses and distortion. Weld bead geometries are outcomes of several welding parameters taken into consideration. If arc travel is high and arc power is kept low it will produce very low fusion. If electrode feed rate is kept higher width is also found to be on higher side which makes bead tto flat. Also, the parameters like current, voltage, arc travel rate, polarity affects weld bead geometry. Hence, this paper uses techniques like ANN, linear regression and curvilinear regression for predictions of weld bead geometry and their relations with different weld parameters. I. INTRODU

Using Artificial Neural Networks to Predict the Effect of Input Parameters on Weld Bead Geometry for SAW Process

2021 ◽  
Vol 54 (2) ◽  
pp. 309-315
Author(s):  
Majid Midhat Saeed ◽  
Ziad Shakeeb Al Sarraf
Keyword(s):  
Neural Network ◽  
Arc Welding ◽  
Welding Process ◽  
Submerged Arc Welding ◽  
Weld Bead ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
The Neural Network ◽  
Input Parameters ◽  
Submerged Arc

Based on high quality and reliability, one of the most efficient methods for joining metals is Submerged Arc Welding (SAW). In this presented work, an attempt has been successfully taken to develop a model to predict the effect of input parameters on weld bead geometry of submerged arc welding process with the help of neural network technique and analysis of various process control variables and important of weld bead parameters in submerged arc welding. The complexity non-linear relationships of input / output variables for any computational models can be addressed by using artificial neural networks (ANN). Today, ANN represents a powerful modeling technique, that depend on statistical approach, presently practiced in many fields of engineering for modeling complex relationships that other physical models cannot be explained it easily. A welding process with automatic or semiautomatic is required to complete the weld through using tubular electrode with consumable flux. Parameters such as welding current, welding speed and voltage are influenced on the quality of the joints. The work conducts many experiments; these are basically depending on many factors and levels. A selection of 2205 duplex stainless steel is carried out in this study to conduct three factors and five levels of central composite design. Neural network model structure having number of neurons layers such as (3 input layers, 1 hidden layer and 3 output layers) with back propagation algorithm has been successfully applied to extract weld bead geometry from predicting the effect of input parameters. Good agreement was obtained between predicted and experiment results, however process parameters such as speed shows opposite effect on all weld parameters. It was seen that weld height and width are proportional to the amount of input current. The prediction of the neural network model showed excellent agreement with the actual results, which indicate that the neural network is viable means for predicting of not only weld bead geometry, but also other parameters such as polarity, current type and flux geometry. This recommends setting the neural network to be applicable for real time work.

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