Abstract

A subsection interpolation method based on the curve curvature threshold is proposed to resolve the incompatible problem of machining accuracy and machining efficiency in parametric curve machining. In the pre-interpolation stage, the curve curvature threshold is calculated based on geometric and kinematic constraints. The subsection interpolation key points and their nominal velocities are then determined from the curvature threshold points and the start and end points of the curve, and the arc length of each subsegment can be calculated based on the adaptive Simpson method. As a result, the S-type speed planning algorithm and the bidirectional speed scanning algorithm are used to update and realize the global speed curve to reduce the speed fluctuation. In the real-time interpolation stage, the curve interpolation parameters are calculated using the parametric modified second-order Runge–Kutta method, which could improve the interpolation accuracy significantly and also shorten the interpolation time. Finally, it is found using numerical cases that the proposed method can smooth the overall interpolation speed, reduce the speed fluctuation effectively and improve the real-time performance of the interpolation.

Details

Title
An efficient and accurate interpolation method for parametric curve machining
Author
Wei, Juan 1 ; Sun, Chao 2 ; Zhang, Xue-jing 2 ; Wang, Er-jie 2 ; Law, Deify 3 

 Xi’an University of Science and Technology, College of Mechanical Engineering, Xi’an, China (GRID:grid.440720.5) (ISNI:0000 0004 1759 0801); Shaanxi Provincial Key Laboratory of Mine Electromechanical Equipment Intelligent Monitoring, Xi’an, China (GRID:grid.440720.5) 
 Xi’an University of Science and Technology, College of Mechanical Engineering, Xi’an, China (GRID:grid.440720.5) (ISNI:0000 0004 1759 0801) 
 California State University, Fresno, Department of Mechanical Engineering, Fresno, USA (GRID:grid.253558.c) (ISNI:0000 0001 2309 3092) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2718028528
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.