Research Papers

Table-Based Volumetric Error Compensation of Large Five-Axis Machine Tools

[+] Author and Article Information
Jennifer Creamer

The Boeing Company,
P.O. Box 516,
St. Louis, MO 63166
e-mail: jennifer.r.creamer@boeing.com

Patrick M. Sammons

Department of Mechanical
and Aerospace Engineering,
Missouri University of Science and Technology,
194 Toomey Hall,
400 W 13th Street,
Rolla, MO 65401
e-mail: pmsd44@mst.edu

Douglas A. Bristow

Department of Mechanical
and Aerospace Engineering,
Missouri University of Science and Technology,
194 Toomey Hall,
400 W 13th Street,
Rolla, MO 65401
e-mail: dbristow@mst.edu

Robert G. Landers

Department of Mechanical
and Aerospace Engineering,
Missouri University of Science and Technology,
194 Toomey Hall,
400 W 13th Street,
Rolla, MO 65401
e-mail: landersr@mst.edu

Philip L. Freeman

The Boeing Company,
4249 Crosspoint Dr.,
Ladson, SC 29456
e-mail: philip.l.freeman@boeing.com

Samuel J. Easley

The Boeing Company,
P.O. Box 516,
St. Louis, MO 63166
e-mail: samuel.j.easley@boeing.com

Manuscript received December 2, 2015; final manuscript received July 1, 2016; published online September 21, 2016. Assoc. Editor: Xiaoping Qian.

J. Manuf. Sci. Eng 139(2), 021011 (Sep 21, 2016) (11 pages) Paper No: MANU-15-1631; doi: 10.1115/1.4034399 History: Received December 02, 2015; Revised July 01, 2016

This paper presents a geometric error compensation method for large five-axis machine tools. Compared to smaller machine tools, the longer axis travels and bigger structures of a large machine tool make them more susceptible to complicated, position-dependent geometric errors. The compensation method presented in this paper uses tool tip measurements recorded throughout the axis space to construct an explicit model of a machine tool's geometric errors from which a corresponding set of compensation tables are constructed. The measurements are taken using a laser tracker, permitting rapid error data gathering at most locations in the axis space. Two position-dependent geometric error models are considered in this paper. The first model utilizes a six degree-of-freedom kinematic error description at each axis. The second model is motivated by the structure of table compensation solutions and describes geometric errors as small perturbations to the axis commands. The parameters of both models are identified from the measurement data using a maximum likelihood estimator. Compensation tables are generated by projecting the error model onto the compensation space created by the compensation tables available in the machine tool controller. The first model provides a more intuitive accounting of simple geometric errors than the second; however, it also increases the complexity of projecting the errors onto compensation tables. Experimental results on a commercial five-axis machine tool are presented and analyzed. Despite significant differences in the machine tool error descriptions, both methods produce similar results, within the repeatability of the machine tool. Reasons for this result are discussed. Analysis of the models and compensation tables reveals significant complicated, and unexpected kinematic behavior in the experimental machine tool. A particular strength of the proposed methodology is the simultaneous generation of a complete set of compensation tables that accurately captures complicated kinematic errors independent of whether they arise from expected and unexpected sources.

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Fig. 1

Illustration of nominal and actual axis coordinate frames where Ek describes transformation from nominal frame k to actual frame k'

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Fig. 2

Industrial five-axis machine tool used for experimental studies conducted in this paper

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Fig. 3

Diagram of axis kinematics for industrial five-axis machine tool

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Fig. 4

Standard deviation for laser tracker repeatability

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Fig. 5

Illustration of tool length measurement

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Fig. 8

Compensation table functions generated from AP and 6DoF models

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Fig. 9

Histogram of residuals between measured and commanded positions using the generated compensation tables

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Fig. 6

Distribution of measurement points used for model identification. Large circles show where points were removed due to collision avoidance (CA) and line of sight (LOS) constraints.

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Fig. 7

Histogram of identification measurements for nominal, 6DoF, and AP models

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Fig. 10

Experimental results for rotation test



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