Geometric deviation reduction method for interpolated toolpath in five-axis flank milling of the S-shaped test piece. (April 2020)

• Record Type: Journal Article
• Authors: Wang, Liping; Li, Weitao; Si, Hao; Yuan, Xing; Liu, Yuzhe
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 234:Number 5(2020)
• Page Start: 910
• Page End: 919
• Publication Date: 2020-04
• Subjects: Five-axis flank milling -- geometric deviation -- interpolated toolpath -- cutter location data optimization -- S-shaped test piece; Mechanical engineering -- Periodicals; Engineering -- Management -- Periodicals; Manufacturing processes -- Periodicals; 629.8
• DOI: 10.1177/0954405419889235
• Languages: English
• ISSNs: 0954-4054

Abstract

Geometric deviation, defined as the distance between the designed surface and the machined surface, is an important component of machining errors in five-axis flank milling of the S-shaped test piece. Since the interpolated toolpath in practical machining process is the approximation of the theoretical toolpath, the geometric deviation caused by the interpolated toolpath appears. To overcome this problem, a novel geometric deviation reduction method is suggested in this study. First, the features of the S-shaped test piece are analyzed. Second, the theoretical toolpath is generated according to the designed surface and the cutter location data is obtained by discretizing the theoretical toolpath. The linear interpolation of the cutter location data is carried out to obtain the interpolated toolpath. Then, the geometric deviation is modeled by calculating the Hausdorff distance between the tool axis trajectory surface based on the interpolated toolpath and the offset surface of the designed surface. Finally, the geometric deviation is reduced by optimizing the cutter location data without inserting more cutter location points. The machining experiment is conducted to verify the effectiveness of the proposed method. The experimental results agree with the simulation results, and both of them indicate the geometric deviation on the machined surface reduces after optimization.