scholarly journals Prediction of Flux Core Arc Welding (FCAW) Bead Geometry and Welding Parameters for 1G Position

2017 ◽  
Vol 5 (1) ◽  
pp. 55-58
Author(s):  
M. F. Abdul Razak ◽  
◽  
Abdul Ghalib ◽  
Abdullah Abdullah ◽  
M. A. H. Ramli ◽  
...  
Keyword(s):  
Arc Welding ◽  
Welding Parameters ◽  
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.

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 Study on Welding Quality of Robotic Arc Welding Process Using Mahalanobis Distance Method

2013 ◽  
Vol 773-774 ◽  
pp. 759-765 ◽  
Author(s):  
Reenal Ritesh Chand ◽  
Ill Soo Kim ◽  
Ji Hye Lee ◽  
Jong Pyo Lee ◽  
Ji Yeon Shim ◽  
...  
Keyword(s):  
Process Parameters ◽  
Mahalanobis Distance ◽  
Arc Welding ◽  
Gma Welding ◽  
Welding Process ◽  
Experimental Results ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Welding Quality ◽  
Arc Welding Process

In robotic GMA (Gas Metal Arc) welding process, heat and mass inputs are coupled and transferred by the weld arc and molten base material to the weld pool. The amount and distribution of the input energy are basically controlled by the obvious and careful choices of welding process parameters in order to accomplish the optimal bead geometry and the desired mechanical properties of the quality weldment. To make effective use of automated and robotic GMA welding, it is imperative to predict online faults for bead geometry and welding quality with respect to welding parameters, applicable to all welding positions and covering a wide range of material thickness. To successfully accomplish this objective, two sets of experiment were performed with different welding parameters; the welded samples from SM 490A steel flats adopting the bead-on-plate technique were employed in the experiment. The experimental results of current and voltage waveforms were used to predict the magnitude of bead geometry and welding quality, and to establish the relationships between weld process parameters and online welding faults. MD (Mahalanobis Distance) technique is employed for investigating and modeling of GMA welding process and significance test techniques were applied for the interpretation of the experimental data. Statistical models developed from experimental results which can be used to control the welding process parameters in order to achieve the desired bead geometry based on weld quality criteria.

Correlation between Welding Parameter and Bead Geometry of Flux Cored Arc Welding (FCAW) in Horizontal Position (2F)

2014 ◽  
Vol 564 ◽  
pp. 549-554
Author(s):  
Nik Mohd Baihaki Abd Rahman ◽  
Abdul Ghalib Tham ◽  
Sunhaji Kiyai Abas ◽  
Razali Hassan ◽  
Yupiter H.P. Manurung ◽  
...  
Keyword(s):  
Heat Input ◽  
Arc Welding ◽  
Weld Bead ◽  
Welding Parameter ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Automatic Welding ◽  
Trend Line ◽  
Flux Cored Arc Welding ◽  
Good Prediction Accuracy

A robotic system can convert the semi-automatic Flux Cored Arc Welding (FCAW) to an automatic welding system. The critical requirement in automated welding process is that the optimal welding parameter has to be set before welding start. These input welding parameters cannot be easily guessed unless one has the knowledge. Only very specific range of heat input that produces quality weld deposition. The correlation between the heat input and fillet weld bead can be displayed in a unique trend-line graph. Mathematical formulas that match the trend-line profile can be used to create a prediction calculator that displays the digital values of weld bead geometry when welded at a specific range of heat input. Small Mean Absolute Deviation between predicted and measured geometry means good prediction accuracy. With this correlation chart, the welding parameter for quality weld bead can be selected and the geometry of FCAW weld deposition in 2F position can be predicted accurately without trial and error.

Effect of Minor Additives on Bead Geometry and Shape Relationship Using Submerged Arc Welding Fluxes

2015 ◽  
Vol 15 (2) ◽  
pp. 183-196
Author(s):  
Brijpal Singh ◽  
Zahid Akhtar Khan ◽  
Arshad Noor Siddiquee
Keyword(s):  
Arc Welding ◽  
Travel Speed ◽  
Submerged Arc Welding ◽  
Welding Parameters ◽  
Deep Penetration ◽  
Bead Geometry ◽  
Weld Penetration ◽  
Design Expert ◽  
Welding Fluxes ◽  
Submerged Arc

AbstractSubmerged arc welding (SAW) is extensively used because of its ability of metal refinement, deep penetration and protection from atmospheric contamination. In this study 20 base fluxes having CaO–SiO2–Al2O3 were prepared by agglomeration technique and CaF2, FeMn and NiO were added to study their effects on bead geometry (weld width, reinforcement and penetration) and shape relationship parameters (weld penetration shape factor and weld reinforcement form factor). The bead on plate welds was made on 18 mm thick MS plates using submerged arc welding. Welding parameters voltage, current and travel speed were kept constant. Design expert 8.0.7.1 has been used to develop mathematical models and ANOVA was used to check the accuracy and significance of the developed models. In this study the elements transferred to the welds during submerged arc welding have been correlated with the bead geometry.

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.

Sign in / Sign up

Export Citation Format

Share Document