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Separating machining errors of S-shaped samples based on the comprehensive error field of five-axis machine tools

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Abstract

In the present study, a new method is proposed to separate machining errors of a 5-axis machine tool with a double turntable. Moreover, a high-precision compensation model is established to calculate machining errors of thin-walled parts in a 5-axis machine tool. In this regard, a comprehensive error model of the S-shaped sample part considering geometric, thermal, and machining errors is established. Then based on the homogeneous transformation matrix, the geometric error models of translational axes and the A and C axes of the rotation axis are established. Accordingly, geometric errors of each motion axes are identified and measured. Meanwhile, the thermal error of the spindle is measured, and the thermal error model of the machine tool along X/Y/Z directions is established using the multiple linear regression method. Finally, the S-shaped sample is processed and the on-machine measurement of the sample surface is carried out. Based on the obtained machining error from the separation method, the distribution of the total surface error at different curvatures of the S-shaped sample is analyzed, and the distributions of geometric and thermal errors of the machine tool are obtained. It is found that when the geometric and thermal errors of the machine tool are compensated, the measured machining error of each point of the S-shaped sample can be reduced by 10–15 µm compared with that before compensation.

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Abbreviations

σ TE(x, y, z, ΔT, t 0):

Total error of the machine

δ x(C), δ y(C), δ Z(C):

The average line error of the C-axis along the x, y, and z axes

σ G(x, y, z):

Geometric error of the machine

ε x(x), ε y(x), ε z(x):

The average angular error of the x-axis along the x, y, and z axes

σ T(ΔT, t 0):

Thermal error of the machine

ε x(y), ε y(y), ε z(y):

The average angular error of the y-axis along the x, y, and z axes

σ M(x, y, z):

Errors in the machining process of the machine tool

ε x(z), ε y(z), ε z(z):

The average angular error of the z-axis along the x, y, and z axes

δ x(x), δ y(x), δ z(x):

The average line error of the x-axis along the x, y, and z axes

ε x(A), ε y(A), ε z(A):

The average angular error of the A-axis rotation around x, y, z axes

δ x(y), δ y(y), δ z(y):

The average line error of the y-axis along the x, y, and z axes

ε x(C), ε y(C), ε z(C):

The average angular error of the C-axis rotation around x, y, z axes

δ x(z), δ y(z), δ z(z):

The average line error of the z-axis along the x, y, and z axes

ε xy, ε yz, ε xz, ε Ay, ε Az, ε Cx, ε Cy :

Squareness error

δ x(A), δ y(A), δ z(A):

The average line error of the A-axis along the x, y, and z axes

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Acknowledgments

This research was supported by the State Key Program of National Natural Science Foundation of China (51720105009).

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Authors and Affiliations

  1. Key Laboratory of Advanced Manufacturing Intelligent Technology of Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, China

    Shi Wu, Yupeng Wang, Xianli Liu, Zhengdong Fan & Tai Yu

Authors
  1. Shi Wu
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  2. Yupeng Wang
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  3. Xianli Liu
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  4. Zhengdong Fan
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  5. Tai Yu
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Corresponding author

Correspondence to Shi Wu.

Additional information

Shi Wu (corresponding author) is a post doctor of the School of Department of Applied Mechanics, Belarusian State University, The Republic of Belarus. He received his Ph.D. in Mechanical Engineering from Harbin University of Science and Technology, China. His main research interests include mechanical kinetics and on-machine Inspection.

Yupeng Wang graduated from Harbin University of Science and Technology with a master’s degree in 2020. He is now a doctoral candidate at Harbin University of Science and Technology. He mainly engaged in error analysis and modeling research.

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Wu, S., Wang, Y., Liu, X. et al. Separating machining errors of S-shaped samples based on the comprehensive error field of five-axis machine tools. J Mech Sci Technol 37, 305–316 (2023). https://doi.org/10.1007/s12206-022-1230-3

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  • DOI: https://doi.org/10.1007/s12206-022-1230-3

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