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Selection of limited and constrained compensation tables for five-axis machine tools

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Abstract

Machine tool geometric inaccuracies are frequently corrected through the use of compensation tables available in machine tool controllers. Each compensation table contains a set of values that determine the incremental change in the commanded position of an axis given the current positions of the axes. While a five-axis machine tool, for example, can have at most 25 compensation tables, most machine tool controllers limit the number of compensation tables that can be implemented and provide constraints on the combinations of compensation tables that can be utilized. This work presents an artificial intelligence-based methodology to select and populate the optimal set of machine tool compensation tables when these limitations and constraints exist. Using data from an industrial five-axis machine tool to construct a kinematic error model, simulation results for the proposed methodology and a heuristic based on the impact of individual compensation tables when selecting six compensation tables are compared, and the proposed methodology is found to outperform the heuristic. The proposed methodology and a solution based on a full set of compensation tables are experimentally implemented on the machine tool and the mean volumetric error resulting from the proposed methodology is found to be only 25 μm less than the volumetric error resulting from the full set of tables. The proposed methodology is then implemented in two more simulation studies where constraints are imposed on which combination of compensation tables could be used and which type of compensation tables could not be utilized. The resulting mean volumetric error was 7.0 and 28.3 μm greater, respectively, than the unconstrained solution.

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References

  1. 1.

    ISO 230-1 (2012) Test code for machine tools part I: geometric accuracy of machine tools operating under no-load or quasi-static conditions. ISO, Geneva

  2. 2.

    Sartori S, Zhang GX (1995) Geometric error measurement and compensation of machines. CIRP Ann Manuf Technol 44(2):599–609

  3. 3.

    Schwenke H, Knapp W, Haitjema H, Weckermann A, Schmitt R, Delbessine F (2008) Geometric error measurement and compensation of machines—an update. CIRP Annals-Manufacturing Technology 57(2):660–675

  4. 4.

    Ibaraki S, Knapp W (2012) Indirect measurement of volumetric accuracy for three-axis and five axis machine tools: a review. Int J Autom Technol 6(2):110–124

  5. 5.

    Khan AW, Chen W (2011) A methodology for systematic geometric error compensation in five-axis machine tools. Int J Adv Manuf Technol 53:615–628

  6. 6.

    Cheng Q, Zhao H, Zhang G, Gu P, Cai L (2014) An analytical approach for crucial geometric errors identification of multi-axis machine tool based on global sensitivity analysis. Int J Adv Manuf Technol 75:107–121

  7. 7.

    Lin Y, Shen Y (2003) Modelling of five-axis machine tool metrology models using the matrix summation approach. Int J Adv Manuf Technol 21:243–248

  8. 8.

    P. Freeman (2006) A novel means of software compensation for robots and machine tools. In: Aerospace manufacturing and automated fastening conference and exhibition, Toulouse

  9. 9.

    Wang Z, Mastrogiacomo L, Franceschini F, Maropoulos P (2011) Experimental comparison of dynamic tracking performance of iGPS and laser tracker. Int J Adv Manuf Technol 56:205–213

  10. 10.

    Creamer J, Sammons PM, Bristow DA, Landers RG (2017) Table-based volumetric error compensation of large 5-axis machine tools. ASME Journal of Manufacturing Science and Engineering 139(1):021011–021011 11

  11. 11.

    B. Mooring (1983) The effect of joint axis misalignment on robot positioning accuracy. In: Proceedings of the 1983 ASME Computers in Engineering Conference: Chicago

  12. 12.

    Mooring B, Roth ZS, Driels MR (1991) Fundamentals of manipulator calibration. Wiley, Hoboken

  13. 13.

    Hollerbach C, Wampler J, Arai T (1995) An implicit loop method for kinematic calibration and its application to closed-chain mechanisms. IEEE Trans Robotics and Automation 11(5):710–724

  14. 14.

    Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Publishing Company, Inc., Reading

  15. 15.

    P. S. Efraimidis and P. Spirakis (2008) Weighted random sampling. In: Encyclopedia of algorithms. Springer pp. 1–99

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Correspondence to R. G. Landers.

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Creamer, J., Bristow, D.A. & Landers, R.G. Selection of limited and constrained compensation tables for five-axis machine tools. Int J Adv Manuf Technol 92, 1315–1327 (2017). https://doi.org/10.1007/s00170-017-0230-4

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Keywords

  • Volumetric error
  • Geometric error compensation
  • Five-axis machine tools