research-article

Process parameter optimization of lap joint fillet weld based on FEM-RSM-GA integration technique

Authors:

M. Rais-Rohani,

K. MotoyamaAuthors Info & Claims

Advances in Engineering Software, Volume 79, Issue C

Pages 127 - 136

https://doi.org/10.1016/j.advengsoft.2014.09.007

Published: 01 January 2015 Publication History

Abstract

A welding process design tool is proposed for arc welding parametric optimization.It is based on integrated Finite Element Method, Response Surface Method and Genetic Algorithms.Simulation based process parameter optimization is possible without expensive experiments.The method effectively determines optimum parameters for minimum distortion. This study introduces a welding process design tool to determine optimal arc welding process parameters based on Finite Element Method (FEM), Response Surface Method (RSM) and Genetic Algorithms (GA). Here, a sequentially integrated FEM-RSM-GA framework has been developed and implemented to reduce the weld induced distortion in the final welded structure. It efficiently incorporates finite element based numerical welding simulations to investigate the desired responses and the effect of design variables without expensive trial experiments. To demonstrate the effectiveness of the proposed methodology, a lap joint fillet weld specimen has been used in this paper. Four process parameters namely arc voltage, input current, welding speed and welding direction have been optimized to minimize the distortion of the structure. The optimization results revealed the effectiveness of the methodology for welding process design with reduced cost and time.

References

[1]

J. Edwin Raja Dhas, S. Jenkins Hexley Dhas, A review on optimization of welding process, Proc Eng, 38 (2012) 544-554.

[2]

K.Y. Benyounis, A.G. Olabi, Optimization of different welding processes using statistical and numerical approaches - a reference guide, Adv Eng Softw, 39 (2008) 483-496.

Digital Library

[3]

S.B. Brown, H. Song, Finite element simulation of welding of large structures, ASME J Eng Ind, 114 (1992) 441-451.

[4]

D. Camilleri, T.G.F. Gray, Computationally efficient welding distortion simulation techniques, Model Simulat Mater Sci Eng, 13 (2005) 1365-1382.

[5]

D.S. Correia, C.V. Goncalves, S.S.d. Cunha, V.A. Ferraresi, Comparison between genetic algorithms and response surface methodology in GMAW welding optimization, J Mater Process Technol, 160 (2005) 70-76.

[6]

D.S. Correia, C.V. Goncalves, S.S.C. Junior, V.A. Ferraresi, GMAW welding optimization using genetic algorithms, J Brazil Soc Mech Sci Eng XXVI (2004) 28-33.

[7]

D. Deng, W. Liang, H. Murakawa, Determination of welding deformation in fillet-welded joint by means of numerical simulation and comparison with experimental measurements, J Mater Process Technol, 183 (2007) 219-225.

[8]

D. Deng, H. Murakawa, FEM prediction of buckling distortion induced by welding in thin plate panel structures, Comput Mater Sci, 43 (2008) 591-607.

[9]

D. Deng, H. Murakawa, Prediction of welding distortion and residual stress in a thin plate butt-welded joint, Comput Mater Sci, 43 (2008) 353-365.

[10]

D. Deng, H. Murakawa, W. Liang, Numerical simulation of welding distortion in large structures, Comput Method Appl Mech Eng, 196 (2007) 4613-4627.

[11]

D. Deng, H. Murakawa, W. Liang, Prediction of welding distortion in a curved plate structure by means of elastic finite element method, J Mater Process Technol, 203 (2008) 252-266.

[12]

D. Deng, H. Murakawa, M. Shibahara, Investigations on welding distortion in an asymmetrical curved block by means of numerical simulation technology and experimental method, Comput Mater Sci, 48 (2010) 187-194.

[13]

J. Goldak, A. Chakravarti, M. Bibby, A new finite element model for welding heat source, Metall Trans B, 15B (1984) 299-305.

[14]

J.A. Goldak, M. Akhlaghi, Computational Welding Mechanics, Springer, New York, 2005.

[15]

D.E. Goldberg, Genetic Algorithms in Search, Optimization & Machine Learning, Addison-Wesley, Massachusetts, 1989.

[16]

M. Islam, A. Buijk, M. Rais-Rohani, K. Motoyama, Simulation-based numerical optimization of arc welding process for reduced distortion in welded structures, Finite Elem Anal Des, 84 (2014) 54-64.

[17]

Islam MR, Rohbrecht J, Buijk A, Namazi E, Liu B, Motoyama K. Computational optimization of arc welding parameters using coupled genetic algorithms and finite element method. In: Proceedings of the ASME 2013 international mechanical engineering congress & exposition, San Diego, California, USA; 2013.

[18]

M.H. Kadivar, K. Jafarpur, G.H. Baradaran, Optimizing welding sequence with genetic algorithm, Comput Mech, 26 (2000) 514-519.

[19]

D. Kim, M. Kang, S. Rhee, Determination of optimal welding conditions with a controlled random search procedure, Weld J (2005) 125-130.

[20]

D. Kim, S. Rhee, H. Park, Modeling and optimization of a GMA welding process by genetic algorithm and response surface methodology, Int J Prod Res, 40 (2002) 1699-1711.

[21]

L.-E. Lindgren, Finite element modeling and simulation of welding Part 1: increased complexity, J Therm Stresses, 24 (2001) 141-192.

[22]

L.-E. Lindgren, Finite element modeling and simulation of welding. Part 3: efficiency and integration, J Therm Stresses, 4 (2001) 305-334.

[23]

L.-E. Lindgren, Computational Welding Mechanics, Woodhead Publishing, Cambridge, 2007.

[24]

L.E. Lingren, Finite element modeling and simulation of welding. Part 2: improved material modeling, J Therm Stresses, 24 (2001) 195-231.

[25]

P. Michaleris, A. Debiccari, Predictive technique for buckling analysis of thin section panels due to welding, J Ship Prod, 12 (1996) 269-275.

[26]

P. Michaleris, A. Debiccari, Prediction of welding distortion, Weld J, 76 (1997) 172-181.

[27]

D.C. Montgomery, Design and Analysis of Experiments, John Wiley & Sons, New York, 1997.

[28]

H.-S. Park, X.-P. Dang, Structural optimization based on CAD-CAE integration and metamodeling techniques, Comput Aided Des, 42 (2010) 889-902.

Digital Library

[29]

J. Song, J. Peters, A. Noor, P. Michaleris, Sensitivity analysis of the thermomechanical response of welded joints, Int J Solids Struct, 40 (2003) 4167-4181.

[30]

J. Song, J.Y. Shanghvi, P. Michalers, Sensitivity analysis and optimization of thermo-elasto-plastic processes with applications to welding side heater design, Comput Method Appl Mech Eng, 193 (2004) 4541-4566.

[31]

R. Sudhakaran, V.V. Murugan, P.S. Sivasakthivel, Optimization of process parameters to minimize angular distortion in gas tungsten arc welded stainless steel 202 grade plates using particle swarm optimization, J Eng Sci Technol, 7 (2012) 195-208.

[32]

C.L. Tsai, S.C. Park, W.T. Cheng, Welding distortion of a thin-plate panel structure, Weld Res Suppl (1999) 156-165.

Cited By

Zhang FZhou T(2019)Process parameter optimization for laser-magnetic welding based on a sample-sorted support vector regressionJournal of Intelligent Manufacturing10.1007/s10845-017-1378-330:5(2217-2230)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10845-017-1378-3
Li XLi WHe F(2018)A multi-granularity NC program optimization approach for energy efficient machiningAdvances in Engineering Software10.1016/j.advengsoft.2017.08.014115:C(75-86)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1016/j.advengsoft.2017.08.014
Zhou QCao LZhou HHuang X(2018)Prediction of angular distortion in the fiber laser keyhole welding process based on a variable-fidelity approximation modeling approachJournal of Intelligent Manufacturing10.1007/s10845-018-1391-129:3(719-736)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s10845-018-1391-1
Show More Cited By

Process parameter optimization of lap joint fillet weld based on FEM-RSM-GA integration technique

Recommendations

Simulating fabrication and imprinting distortions in step-and-flash imprint lithography templates
Proceedings of the 29th international conference on micro and nano engineering

Step-and-flash imprint lithography (SFIL) is a novel approach for producing chips with sub-45-nm features. The technology uses a quartz template to imprint a dispensed polymer solution on the device wafer in a 1 × pattern-transfer process. The accuracy ...
Process optimization of gold stud bump manufacturing using artificial neural networks

The optimal operating conditions of a gold stud bumping process were determined using a process optimization scheme for a microelectronic packaging foundry. The schematic procedure of the process optimization is first to evaluate effects of the ...
Process optimisation of a MEMS based PZT actuated microswitch

Graphical abstractDisplay Omitted The optimised fabrication process for a PZT actuated microswitch was developed.A comparison between standard ad optimised process was carried out.The actuation properties of the device were evaluated.The optimised ...

Comments

Information & Contributors

Information

Published In

cover image Advances in Engineering Software

Advances in Engineering Software Volume 79, Issue C

January 2015

148 pages

ISSN:0965-9978

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Elsevier Science Ltd.

United Kingdom

Publication History

Published: 01 January 2015

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhang FZhou T(2019)Process parameter optimization for laser-magnetic welding based on a sample-sorted support vector regressionJournal of Intelligent Manufacturing10.1007/s10845-017-1378-330:5(2217-2230)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10845-017-1378-3
Li XLi WHe F(2018)A multi-granularity NC program optimization approach for energy efficient machiningAdvances in Engineering Software10.1016/j.advengsoft.2017.08.014115:C(75-86)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1016/j.advengsoft.2017.08.014
Zhou QCao LZhou HHuang X(2018)Prediction of angular distortion in the fiber laser keyhole welding process based on a variable-fidelity approximation modeling approachJournal of Intelligent Manufacturing10.1007/s10845-018-1391-129:3(719-736)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s10845-018-1391-1
Zhou QRong YShao XJiang PGao ZCao L(2018)Optimization of laser brazing onto galvanized steel based on ensemble of metamodelsJournal of Intelligent Manufacturing10.1007/s10845-015-1187-529:7(1417-1431)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s10845-015-1187-5
Zhou QYang YJiang PShao XCao LHu JGao ZWang C(2017)A multi-fidelity information fusion metamodeling assisted laser beam welding process parameter optimization approachAdvances in Engineering Software10.1016/j.advengsoft.2017.04.001110:C(85-97)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1016/j.advengsoft.2017.04.001
Zhou QShao XJiang PGao ZWang CShu L(2016)An active learning metamodeling approach by sequentially exploiting difference information from variable-fidelity modelsAdvanced Engineering Informatics10.1016/j.aei.2016.04.00430:3(283-297)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1016/j.aei.2016.04.004
Jiang PWang CZhou QShao XShu LLi X(2016)Optimization of laser welding process parameters of stainless steel 316L using FEM, Kriging and NSGA-IIAdvances in Engineering Software10.1016/j.advengsoft.2016.06.00699:C(147-160)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1016/j.advengsoft.2016.06.006

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents