Coupled Computational Fluid Dynamic and Finite Element Multiphase Modeling of Laser Weld Bead Geometry Formation and Joint Strengths

2013 ◽  
Vol 135 (1) ◽  
Author(s):  
S. Marimuthu ◽  
R. M. Eghlio ◽  
A. J. Pinkerton ◽  
L. Li
Keyword(s):  
Laser Welding ◽  
Fluid Dynamic ◽  
Weld Bead ◽  
Surface Topology ◽  
Fibre Laser ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Weld Quality ◽  
Weld Bead Geometry ◽  
Cross Sectional

Laser welding is used extensively in industry for joining various materials in the assembly of components and structures. Localized melting followed by rapid cooling results in the formation of a weld bead and generation of residual stress. Selection of the appropriate combination of input parameters and understanding their effects is important to achieve the required weld quality with a smooth welding surface. In the present work, a sequentially coupled thermo-structural multiphase analysis was carried out with the objectives of predicting the effect of laser parameters on the change in surface topology of the weld bead and its subsequent effect on structural properties. The work shows that the laser welding parameters strongly affect the weld bead shape, which eventually affects the weld quality. A net shaped weld bead demonstrates better performance in terms of stress distribution and distortion than other weld bead shapes. The numerical simulation results were compared with the experimental observations performed on a mild steel sheet using a fibre laser and the results are in good agreement in terms of weld bead cross-sectional profile and strength.

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.

Optimization of weld bead geometry in laser welding with filler wire process using Taguchi’s approach

2012 ◽  
Vol 44 (7) ◽  
pp. 2020-2025 ◽  
Author(s):  
Yang dongxia ◽  
Li xiaoyan ◽  
He dingyong ◽  
Nie zuoren ◽  
Huang hui
Keyword(s):  
Laser Welding ◽  
Weld Bead ◽  
Bead Geometry ◽  
Filler Wire ◽  
Weld Bead Geometry

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.

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

scholarly journals Modeling and Analysis of the Weld Bead Geometry in Submerged Arc Welding by Using Adaptive Neurofuzzy Inference System

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Nuri Akkas ◽  
Durmuş Karayel ◽  
Sinan Serdar Ozkan ◽  
Ahmet Oğur ◽  
Bayram Topal
Keyword(s):  
Welding Speed ◽  
Weld Bead ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Inference System ◽  
Modeling And Analysis ◽  
Arc Voltage ◽  
Arc Current ◽  
Artificial Neural Network Ann

This study is aimed at obtaining a relationship between the values defining bead geometry and the welding parameters and also to select optimum welding parameters. For this reason, an experimental study has been realized. The welding parameters such as the arc current, arc voltage, and welding speed which have the most effect on bead geometry are considered, and the other parameters are held as constant. Four, three, and five different values for the arc current, the arc voltage, and welding speed are used, respectively. So, sixty samples made of St 52-3 material were prepared. The bead geometries of the samples are analyzed, and the thickness and penetration values of the weld bead are measured. Then, the relationship between the welding parameters is modeled by using artificial neural network (ANN) and neurofuzzy system approach. Each model is checked for its adequacy by using test data which are selected from experimental results. Then, the models developed are compared with regard to accuracy. Also, the appropriate welding parameters values can be easily selected when the models improve.

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.

Determining the relationship between welding parameters, pool width and weld bead geometry using high speed filming

2007 ◽  
Vol 21 (3) ◽  
pp. 220-226
Author(s):  
E J Lima ◽  
Cacarvalho Castro ◽  
A Q Bracarense ◽  
M F Montenegor Campos
Keyword(s):  
High Speed ◽  
Weld Bead ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
The Relationship
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