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A vision-based calibration method for aero-engine blade-robotic grinding system

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Abstract

Robotic grinding has been gradually applied in the field of blade grinding due to its high precision and high flexibility. One important task in robotic grinding is to determine the position of the workpiece coordinate system. In addition, the mounting errors that exist during robotic grinding operations can lead to machining deviations. To solve above problems, this paper presents a vision-based robot workpiece posture error calibration method to fulfill the precision grinding requirements of aero-engine blades. Firstly, the 3D laser scanner is used to obtain point cloud information of aero-engine blades, where the blades are fixed to the end flange of the robot. Due to environmental interference and the limitation of the scanning angle, the initial point cloud is large and redundant; thus, this study applies statistical filtering, voxel filtering, and cluster segmentation for pre-processing. Therefore, to solve the problem of low efficiency of traditional ICP for aligning residual point clouds, this paper proposes to use the trimmed ICP algorithm to improve the alignment speed and accuracy between the obtained point cloud and the discrete CAD model point cloud to calibrate the pose errors of aero-engine blades. The experiments verified that the calibration method proposed in this paper has high precision and good surface machining consistency. The overall surface roughness of the blade is reduced from over Ra8μm to about Ra0.4 μm, which meets the technological requirements.

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Data availability

The datasets generated during and/or analyzed during the current study are not publicly available due to confidentiality of aero-engine blade-related data but are available from the corresponding author on reasonable request.

Code availability

The code generated during and/or analyzed during the current study are not publicly available due to confidentiality of aero-engine blade-related data but are available from the corresponding author on reasonable request.

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Acknowledgements

The authors thank Dr. Shaopeng Niu from the Institute of New Materials, Guangdong Academy of Sciences, for helpful discussion of topics related to this work. In the grinding experiment, thanks to Zixin Mu from the School of Automation, Wuhan University of Technology, for the helpful data analysis. We would like to express our gratitude to our colleagues at Visual Perception and Robot Control Laboratory, Wuhan University of Technology.

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Authors and Affiliations

  1. School of Automation, Wuhan University of Technology, Wuhan, 430070, China

    Chen Chen, Zhenhua Cai, Tingyang Chen, Zifan Li, Fan Yang & Xufeng Liang

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  1. Chen Chen
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  2. Zhenhua Cai
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  3. Tingyang Chen
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  4. Zifan Li
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  5. Fan Yang
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  6. Xufeng Liang
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Contributions

Chen Chen contributed to the idea of the research, carried out the experiments, and completed the manuscript of the paper.

Fan Yang and Xufeng Liang contributed significantly to the analysis and manuscript preparation.

Tingyang Chen and Zifan Li performed the data analyses.

Zhenhua Cai helped perform the analysis with constructive discussions. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Chen Chen.

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Chen, C., Cai, Z., Chen, T. et al. A vision-based calibration method for aero-engine blade-robotic grinding system. Int J Adv Manuf Technol 125, 2195–2209 (2023). https://doi.org/10.1007/s00170-023-10822-6

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