On-machine volumetric-error measurement and compensation methods for micro machine tools

The CNC micro machine tool is an essential of the micro machining process for manufacturing miniature high-tech products. A software-based error compensation method based on measured volumetric errors that can pre-compensate cutting path can further enhance a machine’s accuracy without increasing it...

Full description

Saved in:
Bibliographic Details
Published inInternational journal of precision engineering and manufacturing Vol. 14; no. 6; pp. 989 - 994
Main Authors Wang, Shih-Ming, Lin, Ji-Jun
Format Journal Article
LanguageEnglish
Published Springer Korean Society for Precision Engineering 01.06.2013
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:The CNC micro machine tool is an essential of the micro machining process for manufacturing miniature high-tech products. A software-based error compensation method based on measured volumetric errors that can pre-compensate cutting path can further enhance a machine’s accuracy without increasing its manufacturing cost. Considering the limited workspace of a micro machine tool and the instrument cost, an on-machine and vision-based measurement method integrating machine vision method with use of 3 COMs and homogeneous transformation method was proposed in this study. Sensitivity analysis was conducted to analyze the influence of the CMOS pixel error to the measurement accuracy. To improve the machining accuracy, the measured errors were directly compensated to the machining trajectory with use of an error compensation method that consists of an element-free error interpolation scheme and a recursive software error compensation scheme. Experiments on a toggle-type micro machine tool were conducted to verify the effectiveness of the proposed methods. The results have shown that the feasibility and effectiveness of the proposed methods. The tracking errors of the machine was further improved from (x = 7.9 μm, y = 4.1 μm) to (x = 5 μm, y = 3.7 μm).
ISSN:2234-7593
2005-4602
DOI:10.1007/s12541-013-0131-x