Dynamic transmission error analysis for a CNC machine tool based on built-in encoders

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

The dynamic transmission error of a CNC machine tool affects the manufacturing precision significantly. It may be caused by many factors, such as the geometric error of the transmission parts, the defects on the tooth flank as well as the torsional vibration of running shafts etc. It is very important to locate where the major source is so as to provide a guidance to reduce the transmission error. In this article, the dynamic behavior of the transmission is obtained by using some built-in encoders of a CNC machine tool. Signals obtained from those encoders are the rotating angles of the shafts varying with time. The transmission error can be estimated by using those signals. Some signal processing approaches are established to analyze those signals to locate the error sources. Vibration is one of the most troubling problems encountered in CNC machine tools, this paper also provides an approach for vibration source identification based on the information obtained from the built-in encoders.

Original languageEnglish
Title of host publicationProceedings - 2011 IEEE International Symposium on Assembly and Manufacturing, ISAM 2011
DOIs
StatePublished - 2011
Event2011 IEEE International Symposium on Assembly and Manufacturing, ISAM 2011 - Tampere, Finland
Duration: 25 May 201127 May 2011

Publication series

NameProceedings - 2011 IEEE International Symposium on Assembly and Manufacturing, ISAM 2011

Conference

Conference2011 IEEE International Symposium on Assembly and Manufacturing, ISAM 2011
Country/TerritoryFinland
CityTampere
Period25/05/1127/05/11

Keywords

  • Built-In Encoders
  • Dynamic Transmission Error
  • Error Identification
  • Instantaneous Angular Acceleration
  • Wavelet Transform

Fingerprint

Dive into the research topics of 'Dynamic transmission error analysis for a CNC machine tool based on built-in encoders'. Together they form a unique fingerprint.

Cite this