Modelling of weld-bead geometry and hardness profile in laser welding of plain carbon steel using neural networks and genetic algorithms

2013 ◽  
Vol 27 (7) ◽  
pp. 656-674 ◽  
Author(s):  
A. Singh ◽  
D.E. Cooper ◽  
N.J. Blundell ◽  
D.K. Pratihar ◽  
G.J. Gibbons
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Carbon Steel ◽  
Laser Welding ◽  
Plain Carbon Steel ◽  
Weld Bead ◽  
Hardness Profile ◽  
Bead Geometry ◽  
Weld Bead Geometry

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

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.

Effect of Process Parameters and Heat Input on Weld Bead Geometry of Laser Welded Titanium Ti-6Al-4V Alloy

2019 ◽  
Vol 969 ◽  
pp. 613-618 ◽  
Author(s):  
Muralimohan Cheepu ◽  
D. Venkateswarlu ◽  
P. Nageswara Rao ◽  
S. Senthil Kumaran ◽  
Narayanan Srinivasan
Keyword(s):  
Laser Welding ◽  
Weld Zone ◽  
Laser Beam Welding ◽  
High Strength ◽  
Industrial Applications ◽  
Weld Bead ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Weld Geometry ◽  
Welding Technique

Laser beam welding is one of the most favorable welding technique and its importance in industry is demanding due to higher welding speeds and lower dimensions and distortions in the welds. Moreover, its high strength to weld geometries and minimal heat affected zones makes favorable for various industrial applications. In the present study, laser welding of titanium alloy was investigated to observe the effects of parameters on the bead geometry and metallurgical properties. The laser power and welding speeds were varied to identify their impact on the formation of weld geometry. The width and depth of the fusion zone is varied with welding conditions. The finer grains identified in weld zone and the width of heat affected zone was significantly changes with laser welding power. The mechanical properties of the weld joint are controlled by obtaining optimum weld bead geometry and width of the head affected zone in the welds.

Modelling weld bead geometry using neural networks for GTAW of austenitic stainless steel

2007 ◽  
Vol 12 (7) ◽  
pp. 649-658 ◽  
Author(s):  
P. Ghanty ◽  
S. Paul ◽  
D. P. Mukherjee ◽  
M. Vasudevan ◽  
N. R. Pal ◽  
...  
Keyword(s):  
Neural Networks ◽  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Weld Bead ◽  
Bead Geometry ◽  
Weld 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.

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