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

Author

Listed:
  • Feng Zhang

    (Huazhong University of Science and Technology
    China Ship Development and Design Center)

  • Taotao Zhou

    (China Ship Development and Design Center)

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.

Suggested Citation

  • Feng Zhang & Taotao Zhou, 2019. "Process parameter optimization for laser-magnetic welding based on a sample-sorted support vector regression," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2217-2230, June.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:5:d:10.1007_s10845-017-1378-3
    DOI: 10.1007/s10845-017-1378-3
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    Citations

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    Cited by:

    1. Kong, Xuefeng & Yang, Jun & Hao, Songhua, 2021. "Reliability modeling-based tolerance design and process parameter analysis considering performance degradation," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    2. Zhen Zhang & Zenan Yang & Chenchong Wang & Wei Xu, 2024. "Accelerating ultrashort pulse laser micromachining process comprehensive optimization using a machine learning cycle design strategy integrated with a physical model," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 449-465, January.
    3. Yang Hui & Xuesong Mei & Gedong Jiang & Fei Zhao & Pengcheng Shen, 2020. "Assembly consistency improvement of straightness error of the linear axis based on the consistency degree and GA-MSVM-I-KM," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1429-1441, August.
    4. Anshuman Kumar Sahu & Siba Sankar Mahapatra, 2021. "Prediction and optimization of performance measures in electrical discharge machining using rapid prototyping tool electrodes," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2125-2145, December.
    5. Roham Sadeghi Tabar & Kristina Wärmefjord & Rikard Söderberg & Lars Lindkvist, 2021. "Critical joint identification for efficient sequencing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 769-780, March.

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