Systematic geometric rigid body error identification of 5-axis milling machines

  • Authors:

  • Erik L. J. Bohez;Bancha Ariyajunya;Chanin Sinlapeecheewa;Tin Maung Maung Shein;Do Tien Lap;Gustavo Belforte

  • Affiliations:

  • Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand;Burapha University, 169 Long-Hard Bangsaen Road, Tambon Saensook, Amphur Muang, Chonburi 20131, Thailand;Fabrinet Co. Ltd., 294 Moo 8 Vibhavadi-Rangsit Road Kukot, 12130 Lam Luk Ka, Pathumthani, Thailand;Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand;Le Qui Don University, 100, Hoang Quoc Viet Street, Cau Giay District, Hanoi, Viet Nam;Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy

  • Venue:
  • Computer-Aided Design
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

A 5-axis milling machine has 39 independent geometric error components when the machine tool is considered as a set of five rigid bodies. The identification of the deterministic component of the systematic error is very important. It permits one to improve the accuracy close to the repeatability of the machine tool. This paper gives a new way to identify and compensate all the systematic angular errors separately and then use them further to identify the systematic translational error. Identification based on a new mathematical method and a stable numerical solution method is proposed. The model explains from first principles why some error components have no effect in a first order model. The identification of the total angular systematic errors can be done independently from the translation errors. However, the total translation error depends on the angular errors and the translation errors of each machine tool slide. The main problems solved are to find enough linear independent equations and avoid numerical instability in the computation. It is important to separate numerical problems and linear dependence. The very complex equations are first analyzed in symbolic form to eliminate the linear dependencies. The total of linear independent components in the model is reduced from 30 to 26 for the position dependent errors and from 9 to 3 for the position independent components. Secondly, the large system of linear equations is broken down in many smaller systems. The model is tested first with simulated errors modeled as cubic polynomials. An artifact-based identification is proposed and implemented based on drilling holes in various locations and orientations. New ways to measure the volumetric error directly are proposed. Direct measurement of the total volumetric error requires considerably less measurement than measuring all 6 components of each machine slide especially in the case of a 5-axis machine.