Published at : 07 Oct 2022
Volume : IJtech
Vol 13, No 4 (2022)
DOI : https://doi.org/10.14716/ijtech.v13i4.5083
Karim Agrebi | Laboratory of Applied Mechanics and Engineering, University of Tunis EL Manar, National Engineering School of Tunis, BP 37, Le Belvédère, 1002, Tunisia |
Asma Belhadj | Laboratory of Applied Mechanics and Engineering, University of Tunis EL Manar, National Engineering School of Tunis, BP 37, Le Belvédère, 1002, Tunisia |
Jamel Bessrour | Laboratory of Applied Mechanics and Engineering, University of Tunis EL Manar, National Engineering School of Tunis, BP 37, Le Belvédère, 1002, Tunisia |
Mahmoud Bouhafs | Laboratory of Applied Mechanics and Engineering, University of Tunis EL Manar, National Engineering School of Tunis, BP 37, Le Belvédère, 1002, Tunisia |
Welding is widely used in the aerospace, naval and
automotive industries. Since high temperatures are involved in this process,
solid state metallurgical changes are expected. These metallurgical changes can
induce deformations and residual stresses in welded parts. The objective of
this work is to develop a finite element calculation code, under the MATLAB
environment, to predict the evolution of the various metallurgical
transformations during TIG welding of C50 steel plates. In the proposed
calculation procedure, we used Leblond’s equation and Waeckel’s model to
characterize the metallurgical transformations during respectively heating and
cooling stage. We also taken into account the effect of austenitic grain size
on metallurgical transformations evolution. Thermal properties are introduced
according temperature and phase proportions present during welding operation.
Simulation results show that the metallurgical structure in the heat affected
zone (HAZ) is largely related to welding thermal power and the plate preheating
temperature. We compared simulation results to experimental measurements and
the efficiency of the developed computational code was confirmed.
Coupled Thermo-Metallurgical modeling; Experimental study; Finite element simulation; TIG welding
Welding is a necessary industrial process that persistently needs to be
developed. For this aim, numerical prediction of welded parts behaviors is an
alternative, which avoids the cost of the experimental analysis. During welding
processes, located and moving heat source generates, after cooling, important
residual stresses in welded parts. These stresses are the results of the
heterogeneity of deformation due to changes in temperature and in metallurgical
transformation in the different parts of the weld. This work concerns the
modeling and the finite element simulation of these microstructural
transformations during the welding operation of a plate in C50 steel.
Several
authors have implemented thermomechanical and metallurgical models in numerical
calculation codes to study the generation of residual stresses due to
metallurgical transformations during welding. Ronda et al. (2000) presented
the results of a numerical simulation of welding problem based on
electromagnetic, thermal, mechanical and metallurgical modelling. Results of
this numerical simulation are shown
Sun et al. (2019) performed
a series of numerical simulations on the SIMUFACT software, to examine the
variability of welding residual stresses for different materials. The result
shows that the mixed hardening model provides the most accurate prediction of
residual stresses. Zain-Ul-Abdein
et al. (2011) developed a comparative study between two finite
element models established on ABAQUS and SYSWELD. They studied the effect of
metallurgical transformations on residual stresses and distortions induced by
laser beam welding in a T-joint configuration. The results show that
considering metallurgical changes has a negligible effect on the predicted
distortions but not on the residual stresses distribution.
Other researchers have
developed numerical computer codes for the welding process simulation. Hamide
et al. (2008) contributed to developing an adaptive mesh
module for the TRANSWELD software to simulate the thermo-metallurgical coupled
behavior of a fusion line produced on a steel plate by a TIG welding station. Hendili
et al. (2013) contributed to developing a metallurgical
behavior module established in CODE ASTER. They used Leblond's equation to
describe the formation of the austenitic phase during heating stage and the
Waeckel’s model to describe the decomposition of the austenitic phase during
the cooling stage.
Several other studies present
numerical and experimental characterization of welded assemblies. Baskoro
et al. (2017) investigated the influence of
micro-resistance spot welding parameters on the mechanical properties and
failure of an aluminum alloy 1100 nugget. Baskoro et al.
(2011) propose a study comparing particle swarm
optimization with a genetic algorithm for molten pool detection in fixed
aluminum pipe welding. Rupajati
et al. (2021) investigate the characteristics of the lap
shear force and microstructure of micro friction stir spot welding joints.
Our numerical tool
developed under MATLAB environment is a new personalized numerical calculation
code, simple and flexible. With this tool, we have tried to solve some of the
difficulties that exist in using commercial codes to simulate the welding
process. For example, the study of metallurgical behavior or the heat source
moving simulation during welding cannot be done directly on some commercial
software. Indeed, to predict the behavior of parts during welding on ABAQUS for
example, we need to program the metallurgical behavior laws on FORTRAN. In our
case, the metallurgical changes and source moving peculiarities of the welding
processes study are integrated into our numerical calculation code. The
computer code will be used to perform thermomechanical and metallurgical
calculations during the TIG welding operation. This article is about the
development of the thermo-metallurgical model.
In this study, we
developed a numerical calculation code, under the environment of the MATLAB
software, based on a coupled thermo-metallurgical modeling. This tool is a
powerful means that can be used to optimize welding parameters. Numerical
calculations show that the metallurgical transformations during a welding
operation are heterogeneous. They depend on the relative position to the heat
source, the welding power and the initial temperature of the plate. In addition
to the numerical study, experimental investigations are carried out to
characterize the metallurgical structure obtained after the welding operations.
The comparison analysis shows a good agreement between the simulated and
experimental results. The developed model can predict phase transformation in
the heat-affected zone after welding.
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Agrebi, K., Belhadj, A., Bouhafs, M., 2019.
Three-Dimensional Numerical Simulation of a Gas Tungsten Arc Welding Process. International
Journal of Technology, Volume 10(4), pp. 689–699
Baskoro, A.S., Masuda, R., Suga, Y., 2011.
Comparison of Particle Swarm Optimization and Genetic Algorithm for Molten Pool
Detection in Fixed Aluminum Pipe Welding. International Journal of
Technology, Volume 2(1), pp. 74–83
Baskoro, A.S., Muzakki, H., Kiswanto, G.,
Winarto, 2017. Effects of Micro Resistance Spot Welding Parameters on the
Quality of Weld Joints on Aluminum Thin Plate AA 1100. International Journal
of Technology, Volume 8(7), pp. 1306–1313
Deng, D., 2009. FEM Prediction of Welding
Residual Stress and Distortion in Carbon Steel Considering Phase Transformation
Effects. Materials and Design, Volume 30(2), p. 359–366
Hamide, M., Massoni, E., Bellet, M., 2008.
Adaptive Mesh Technique for Thermal–Metallurgical Numerical Simulation of Arc
Welding Processes. International Journal for Numerical Methods in
Engineering, Volume 73 (5), pp. 624–641
Heinze, C., Schwenk, C., Rethmeier, M.,
2012. Numerical Calculation of Residual
Stress Development of Multi-Pass Gas Metal Arc Welding. Journal of
Constructional Steel Research, Volume 72, pp. 12–19
Hendili, S., 2013. Modèles de Comportement Métallurgique
des Aciers (Metallurgical Behavior Models Steels), Code Aster, Clé :
R4.04.01, pp. 1–28
Li, S., Ren, S., Zhang, Y., Deng, D.,
Murakawa, H., 2017. Numerical Investigation of Formation Mechanism of Welding
Residual Stress in P92 Steel Multi-Pass Joints. Journal of Materials
Processing Technology, Volume 244, pp 240–252
Ronda, J., Oliver, G.J., 2000. Consistent Thermo-Mechano-Metallurgical
Model of Welded Steel with Unified Approach to Derivation of Phase Evolution
Laws and Transformation-Induced Plasticity. Computer Methods in Applied
Mechanics and Engineering, Volume 189(2), pp. 361–418
Rupajati, P., Clarissa, K.G., Baskoro, A.S.,
Kiswanto, G., Winarto, 2021. Characteristics of Mechanical Properties and
Microstructure of Micro Friction Stir Spot Welding of AA1100 and Brass. International
Journal of Technology, Volume 12(6), pp. 1302–1311
Sun, J., Hensel, J., Klassen, J.,
Nitschke-Pagel, T., Dilger, K., 2019. Solid-State Phase Transformation and
Strain Hardening on the Residual Stresses in S355 Steel Weldments. Journal
of Materials Processing Technology, Volume 265, pp. 173–184
Xia, J., Jin, H., 2018. Numerical Modeling of
Coupling Thermal–Metallurgical Transformation Phenomena of Structural Steel in
the Welding Process. Advances in Engineering Software, Volume 115, pp.
66–74
Zain-Ul-Abdein, M.,
Nélias, D., Jullien, J-F., Boitout, F., Dischert, L., Noe, X., 2011. Finite Element
Analysis of Metallurgical Phase Transformations in AA 6056-T4 and Their Effects
Upon the Residual Stress and Distortion States of a Laser Welded T-joint. International
Journal of Pressure Vessels and Piping, Volume 88(1), pp. 45–56