Improved Machine Tool Linear Axis Calibration Through Continuous Motion Data Capture

Abstract Machine tool calibration is becoming recognised as an important part of the manufacturing process. The current international standards for machine tool linear axes calibration support the use of quasi-static calibration techniques. These techniques can be time consuming but more importantly a compromise in quality due to the practical restriction on the spatial resolution of target positions on the axis under test. Continuous motion calibration techniques have the potential to dramatically increase calibration quality. Through taking several measurement values per second while the axis under test is in motion, it is possible to measure in far greater detail. Furthermore, since machine tools normally operate in dynamic mode, the calibration data can be more representative if it is captured while the machine is in motion. The drawback to measuring the axis while in motion is the potential increase in measurement uncertainty. In the following paper, different methods of continuous motion calibration are discussed. A time-based continuous motion solution is proposed as well as a novel optimisation and correlation algorithm to accurately fuse the data taken from quasi-static and continuous motion measurements. The measurement method allows for minimal quasi-static measurements to be taken while using a continuous motion measurement to enhance the calibration process with virtually no additional time constraints. The proposed method does not require any additional machine interfacing, making it a more readily accessible solution for widespread machine tool use than other techniques which require hardware links to the CNC. The result of which means a shorter calibration routine and enhanced results. The quasi-static and continuous motion measurements showed correlation to within 1 μm at the quasi-static measurement targets. An error of 13 μm was detailed on the continuous motion, but was missed using the standard test. On a larger, less accurate machine, the quasi-static and continuous motion measurements were on average within 3 μm of each other however, showed a standard deviation of 4 μm which is less than 1% of the overall error. Finally, a high frequency cyclic error was detected in the continuous motion measurement but was missed in the quasi-static measurement.

[1]  Robert Schmitt,et al.  Geometric error measurement and compensation of machines : an update , 2008 .

[2]  Simon Parkinson,et al.  Towards an Optimization Calculation for Preventative and Reactive Calibration Strategies , 2014 .

[3]  Bingru Yang,et al.  Rapid Machine Tool Verification , 2014 .

[4]  Wolfgang Knapp,et al.  Model-based ‘Chase-the-Ball’ Calibration of a 5-Axes Machining Center , 2006 .

[5]  Bijan Shirinzadeh,et al.  The measurement uncertainties in the laser interferometry-based sensing and tracking technique , 2002 .

[6]  Mohsen Moradi Dalvand,et al.  Laser interferometry-based guidance methodology for high precision positioning of mechanisms and robots , 2010 .

[7]  D. Morton,et al.  Dynamic calibration of CNC machine tools , 1997 .

[8]  Heui Jae Pahk,et al.  A new technique for volumetric error assessment of CNC machine tools incorporating ball bar measurement and 3D volumetric error model , 1997 .

[9]  Ranjan Sen,et al.  Performance Evaluation of Multi-Axis CNC Machine Tools by Interferometry Principle using Laser Calibration System , 2012 .

[10]  Aun-Neow Poo,et al.  Error compensation in machine tools — a review: Part II: thermal errors , 2000 .

[11]  Jenq-Shyong Chen,et al.  Geometric error calibration of multi-axis machines using an auto-alignment laser interferometer , 1999 .

[12]  Jun Ni,et al.  A displacement measurement approach for machine geometric error assessment , 2001 .

[13]  Test code for machine tools — Part 7 : Geometric accuracy of axes of rotation , 2012 .

[14]  P. A. McKeown,et al.  Reduction and compensation of thermal errors in machine tools , 1995 .

[15]  Simon Parkinson,et al.  The role of measurement and modelling of machine tools in improving product quality , 2013 .

[16]  Andrew P. Longstaff,et al.  FEA-based design study for optimising non-rigid error detection on machine tools , 2015 .

[17]  Simon Parkinson,et al.  Multi-objective optimisation of machine tool error mapping using automated planning , 2015, Expert Syst. Appl..

[18]  Christian Brecher,et al.  Volumetric thermo-elastic machine tool behavior , 2015, Prod. Eng..

[19]  G.H.J. Florussen,et al.  Dynamic R-test for rotary tables on 5-axes machine tools , 2012 .

[20]  Shih-Ming Wang,et al.  A new high-efficiency error compensation system for CNC multi-axis machine tools , 2006 .

[21]  M. Burdekin,et al.  Dynamic calibration of the positioning accuracy of machine tools and coordinate measuring machines using a laser interferometer , 2003 .

[22]  Allan D. Spence,et al.  Kinematic and geometric error compensation of a coordinate measuring machine , 2000 .

[23]  B. Lu,et al.  A Displacement Method for Machine Geometry Calibration , 1988 .

[24]  Robert Schmitt,et al.  On-the-fly calibration of linear and rotary axes of machine tools and CMMs using a tracking interferometer , 2009 .

[25]  Sylvain Lavernhe,et al.  Evaluation of servo, geometric and dynamic error sources on five axis high-speed machine tool , 2011, ArXiv.

[26]  Wolfgang Knapp,et al.  Machine tool calibration: Geometric test uncertainty depends on machine tool performance , 2009 .

[27]  Heber Ferreira Franco de Castro,et al.  Uncertainty analysis of a laser calibration system for evaluating the positioning accuracy of a numerically controlled axis of coordinate measuring machines and machine tools , 2008 .

[28]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..