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Journal of Intelligent Manufacturing

, Volume 30, Issue 5, pp 2217–2230 | Cite as

Process parameter optimization for laser-magnetic welding based on a sample-sorted support vector regression

  • Feng ZhangEmail author
  • Taotao Zhou
Article
  • 184 Downloads

Abstract

Magnetic field assisted laser welding (LW-MF) shows great potential in the jointing of large structures. The quality of the welding joint in LW-MF largely depends on the selection of process parameters. In this study, an integrated process parameter optimization framework is developed for magnetic field assisted laser welding. Firstly, Taguchi method is selected to generate sample points and the LW-MF experiments are carried out to obtain the bead geometrical characteristics. Secondly, a sample-sorted SVR (SS-SVR) metamodeling approach is developed to make full use of the already-acquired prediction error information for fitting the relationships between multiple process parameters and the bead geometrical characteristics. A detailed comparison between the developed SS-SVR metamodeling approach and existing SVR metamodeling approach for prediction accuracy is performed. Then, the particle swarm optimization is used to solve the process parameters optimization problem, in which the objective function values are predicted by the developed SS-SVR metamodel. Finally, verification experiment is conducted to verify the reliability of the obtained optimal process parameters. Results illustrate that the proposed integrated process parameter optimization framework is effective for obtaining the optimal process parameters and can be used in LW-MF for practical production.

Keywords

Process parameters Magnetic field Laser welding Support vector regression Particle swarm optimization algorithm 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61703385 and International S&T Cooperation Program of China (ISTCP) under Grant No. 2016YFE0121700.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.The State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhanPeople’s Republic of China
  2. 2.China Ship Development and Design CenterWuhanPeople’s Republic of China

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