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A quality improvement method for complex component fine manufacturing based on terminal laser beam deflection compensation

Author

Listed:
  • Dongxiang Hou

    (Beijing University of Posts and Telecommunications)

  • Xiaodong Wang

    (Xi’an Jiaotong University)

  • Qing Song

    (Beijing University of Posts and Telecommunications)

  • Xuesong Mei

    (Xi’an Jiaotong University)

  • Haicheng Wang

    (CNNC Lanzhou Uranium Enrichment Co., Ltd)

Abstract

The multi-axis laser manufacturing equipment is a piece of standard equipment suitable for complex components with hard and brittle material. The laser beam direction inevitably deviates from the coordinate axis of equipment due to the location of laser and optical parts, leading to component’s substrate over-cutting. This paper proposes a compensation method for the laser beam deflection to enhance the accuracy of the laser manufacturing. The deflection measurement was achieved by image and point cloud processing. The proposed method scans the point cloud model and fits the geometric feature to realize the orientation of the laser collimator relative to the equipment. The error of laser beam deflection calculates by the orientation of collimator and the spatial geometric relationship of the displacement and the laser trajectory. An error compensation model is constructed to compensate for dynamic error in the whole motion range during the calculation of equipment motion. The effectiveness of this method is verified through laser beam measurement, compensation and laser manufacturing for a complex component. The results show that the proposed method improves the processing accuracy (91.6 μm versus 316.6 μm) by data statistics.

Suggested Citation

  • Dongxiang Hou & Xiaodong Wang & Qing Song & Xuesong Mei & Haicheng Wang, 2024. "A quality improvement method for complex component fine manufacturing based on terminal laser beam deflection compensation," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 331-341, January.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:1:d:10.1007_s10845-022-02048-2
    DOI: 10.1007/s10845-022-02048-2
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    References listed on IDEAS

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    1. Yilin Guo & Wen Feng Lu & Jerry Ying Hsi Fuh, 2021. "Semi-supervised deep learning based framework for assessing manufacturability of cellular structures in direct metal laser sintering process," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 347-359, February.
    2. Yanxi Zhang & Deyong You & Xiangdong Gao & Congyi Wang & Yangjin Li & Perry P. Gao, 2020. "Real-time monitoring of high-power disk laser welding statuses based on deep learning framework," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 799-814, April.
    3. Sudipto Chaki & Ravi N. Bathe & Sujit Ghosal & G. Padmanabham, 2018. "Multi-objective optimisation of pulsed Nd:YAG laser cutting process using integrated ANN–NSGAII model," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 175-190, January.
    4. Filippo Simoni & Andrea Huxol & Franz-Josef Villmer, 2021. "Improving surface quality in selective laser melting based tool making," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1927-1938, October.
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