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Optimization of laser brazing onto galvanized steel based on ensemble of metamodels

Author

Listed:
  • Qi Zhou

    (Huazhong University of Science & Technology)

  • Youmin Rong

    (Huazhong University of Science & Technology)

  • Xinyu Shao

    (Huazhong University of Science & Technology)

  • Ping Jiang

    (Huazhong University of Science & Technology)

  • Zhongmei Gao

    (Huazhong University of Science & Technology)

  • Longchao Cao

    (Huazhong University of Science & Technology)

Abstract

Laser brazing (LB) provides a promising way to join the galvanized steel in automotive industry for its significant advantages including high speed, small heat-affected zone, and high welding seam quality. The process parameters of LB have significant effects on the bead profile and hence the quality of joint. Since the relationships between the process parameters and bead profile cannot be expressed explicitly, it is impractical to determine the optimal process parameters intuitively. This paper proposes an optimization methodology by combining genetic algorithm (GA) and ensemble of metamodels (EMs) to address the process parameters optimization of the bead profile in LB with crimping butt. Firstly, Taguchi experimental design is adopted to generate the experimental points. Secondly, the relationships between process parameters (i.e., welding speed, wire feed rate, gap) and the bead geometries are fitted using EMs based on the experimental data. The comparative results show that the EMs can take advantage of the prediction ability of each stand-alone metamodel and thus decrease the risk of adopting inappropriate metamodels. Then, the GA is used to facilitate design space exploration and global optimum search. Besides, the main effects and contribution rates of multiple process parameters on bead profile are analyzed. Eventually, the verification experiments are carried out to demonstrate the effectiveness and reliability of the obtained optimal parameters. Overall, the proposed hybrid approach, GA–EMs, exhibits great capability of guiding the actual LB processing and improving welding quality.

Suggested Citation

  • Qi Zhou & Youmin Rong & Xinyu Shao & Ping Jiang & Zhongmei Gao & Longchao Cao, 2018. "Optimization of laser brazing onto galvanized steel based on ensemble of metamodels," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1417-1431, October.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:7:d:10.1007_s10845-015-1187-5
    DOI: 10.1007/s10845-015-1187-5
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    Citations

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    Cited by:

    1. Cheng Yan & Jianfeng Zhu & Xiuli Shen & Jun Fan & Dong Mi & Zhengming Qian, 2020. "Ensemble of Regression-Type and Interpolation-Type Metamodels," Energies, MDPI, vol. 13(3), pages 1-20, February.
    2. Jingchang Li & Longchao Cao & Jiexiang Hu & Minhua Sheng & Qi Zhou & Peng Jin, 2022. "A prediction approach of SLM based on the ensemble of metamodels considering material efficiency, energy consumption, and tensile strength," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 687-702, March.
    3. Wangwang Huang & Xuesong Mei & Gedong Jiang & Dongxiang Hou & Yifei Bi & Yuyan Wang, 2022. "An on-machine tool path generation method based on hybrid and local point cloud registration for laser deburring of ceramic cores," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2223-2238, December.
    4. Deyuan Ma & Ping Jiang & Leshi Shu & Zhaoliang Gong & Yilin Wang & Shaoning Geng, 2024. "Online porosity prediction in laser welding of aluminum alloys based on a multi-fidelity deep learning framework," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 55-73, January.

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