Abstract
Linear motion commands of multi-axis computer numerical control (CNC) machine tools need to be smoothed at the transition corners, because the velocity discontinuities at corners can result in fluctuations on machine tool motions and lead to poor surface quality. However, few studies have reported on the transition between non-line contour. The paper proposes a novel two-step local corner smoothing method for non-line contour transition, including path smoothing and feedrate scheduling. In path smoothing, a non-uniform rational B-spline (NURBS) curve is adopted to smooth the adjacent non-line contour of screw rotor section while constraining the cornering error with the set tolerance. The maximum deviation between the original trajectory and smoothed toolpath can be analytically calculated. In feedrate scheduling, a jerk-limited S-curve feedrate scheduling scheme is developed based on machine tool dynamics. Finally, in order to verify the correctness of the proposed method, a series of numerical simulations and actual machining and measurement experiments are conducted, and the simulations and experimental results have showed the good performance of the proposed algorithm.
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Funding
This work was supported by National Key R&D Program of China (Grant No. 2020YFE0201000) and National Natural Science Foundation of China (Grant No. 52105512).
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All authors contributed to the study conception and design. Calculations and design of experiment scheme were performed by Daoyang Yu. Motion simulations of transition NURBS curves were conducted by Xiao-Qing Tian. The NC machining experiments were performed by Daoyang Yu and Zhi Ding. The screw motor accuracy measurement was performed by Xiao-Yong Huang. The first draft of the manuscript was written by Daoyang Yu. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Yu, DY., Ding, Z., Tian, XQ. et al. Local corner smoothing algorithm for screw motor high-precision machining. Int J Adv Manuf Technol 126, 2117–2127 (2023). https://doi.org/10.1007/s00170-023-11228-0
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DOI: https://doi.org/10.1007/s00170-023-11228-0