Papers

Peer-reviewed
Sep, 2018

Motion accuracy enhancement of five-axis machine tools by modified CL-data

International Journal of Automation Technology
  • Ryuta Sato
  • ,
  • Shogo Hasegawa
  • ,
  • Keiichi Shirase
  • ,
  • Masanobu Hasegawa
  • ,
  • Akira Saito
  • ,
  • Takayuki Iwasaki

Volume
12
Number
5
First page
699
Last page
706
Language
Publishing type
Research paper (scientific journal)
DOI
10.20965/ijat.2018.p0699

© 2018, Fuji Technology Press. All rights reserved. The motion trajectories of machine tools directly influence the geometrical shape of machined workpieces. Hence, improvement in their motion accuracy is required. It is known that machined shape errors occurring in numerical control (NC) machine tools can be compensated for by modifying the CL-data, based on the amount of error calculated by the measurement results of the machined shape of the workpiece. However, by using this method the shape errors cannot be compensated accurately in five-axis machining, because the final machining shape may not reflect the motion trajectory of a tool owing to the motion errors of the translational and rotary axes. In this study, a modification method of the cutter location (CL)-data, based on the amount of motion errors of the tool center-point trajectory during the machining motion, is newly proposed. The simulation and experiment of a wing profile machining motion is performed, to confirm the effectiveness of the proposed method. From the results, we confirm that the motion accuracy can be significantly improved by applying the proposed method.

Link information
DOI
https://doi.org/10.20965/ijat.2018.p0699
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000452535100011&DestApp=WOS_CPL
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85055264880&origin=inward
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85055264880&origin=inward
ID information
  • DOI : 10.20965/ijat.2018.p0699
  • ISSN : 1881-7629
  • eISSN : 1883-8022
  • SCOPUS ID : 85055264880
  • Web of Science ID : WOS:000452535100011

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