A parametric interpolation method based on prediction and iterative compensation. (8th February 2019)

• Record Type: Journal Article
• Authors: Ni, Hepeng; Zhang, Chengrui; Chen, Chao; Hu, Tianliang; Liu, Yanan
• Is Part Of: International journal of advanced robotic systems. Volume 16:Number 1(2019:Jan./Feb.)
• Publication Date: 2019-02-08
• Subjects: Parametric interpolation -- feedrate fluctuation -- length prediction -- historical interpolation knowledge -- iterative compensation -- second-order Taylor's expansion; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881419828188
• Languages: English
• ISSNs: 1729-8806

Abstract

Parametric interpolation for spline plays an increasingly important role in modern manufacturing. It is critical to develop a fast parametric interpolator with high accuracy. To improve the computational efficiency while guaranteeing low and controllable feedrate fluctuation, a novel parametric interpolation method based on prediction and iterative compensation is proposed in this article. First, the feedrate fluctuation and Taylor's expansion are analyzed that there are two main reasons to reduce the calculation accuracy including the truncation errors caused by neglecting the high-order terms and discrepancy errors between the original curve and the actual tool path. Then, to reduce these errors, a novel parametric interpolation method is proposed with two main stages, namely, prediction and iterative compensation. In the first stage, a quintic polynomial prediction algorithm is designed based on the historical interpolation knowledge to estimate the target length used in the second-order Taylor's expansion, which can improve the calculation accuracy and the convergence rate of iterative process. In the second stage, an iterative compensation algorithm based on the second-order Taylor's expansion and feedrate fluctuation is designed to approach the target point. Therefore, the calculation accuracy is controllable and can satisfy the specified value through several iterations. When finishing the interpolation of current period, the historical knowledge is updated to prepare for the following interpolation. Finally, a series of simulations are conducted to evaluate the good performance in accuracy and efficiency of the proposed method.