IJAT Vol.14 No.3 pp. 360-368
doi: 10.20965/ijat.2020.p0360


A Digital Perspective on Machine Tool Calibration

Benjamin Montavon, Philipp Dahlem, Martin Peterek, and Robert H. Schmitt

Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University
Campus-Boulevard 30, Aachen 52074, Germany

Corresponding author

October 22, 2019
March 4, 2020
May 5, 2020
machine tool calibration, internet of production, computational manufacturing

Machine tool calibration and subsequent controller-based compensation are industrially established and research-intensive techniques used to monitor and increase the volumetric performance in high-precision manufacturing. Moreover, a variety of interim performance checks and integrated sensor approaches have been developed to predict volumetric performance degradation and avoid an economically undesirable downtime. However, the fragmentation of data acquisition and management limits the potential for additional insights with respect to the value creation based on existing methods in the field of machine tool calibration. The authors reviewed the former from the perspective of data sources according to the frequency of their contribution to the digital twin of a machine tool, adopting a digital view regarding machine tool calibration within the Internet of Production concept. The latter proposes a semantic separation of cyber physical production systems into four layers: data sources, data access and provisioning, storage and analytics, and user respective agent feedback. To achieve a common representation across different layers, devices, and industrial Internet protocols, a model-based abstraction layer is required, which must be compatible with existing standards within the field, e.g., the ISO 230 series. Utilizing different Internet of Production architectures and platforms, a multitude of parallel analytic applications and an evaluation of complex models are enabled owing to the availability of ample computing resources, among which the machine tool’s numerical controller takes the role of an edge-device injecting the feedback into the production process. A proof-of-concept of a digital approach to machine tool calibration data storage and processing was established based on the software prototype VoluSoft, which implements an ISO 230-1:2012 based abstraction layer in JavaScript Object Notation format, and an evaluation of the kinematic models to estimate the volumetric performance at the functional point. Apart from generating compensation tables, the results are used to project the expected deviation at the tool tip to the computer-aided design-model of a work piece, correlate the error motions using the temperature data acquired by integrated sensors, and estimate the contribution of the volumetric performance limitation to the uncertainty budget of on-machine measurements.

Cite this article as:
B. Montavon, P. Dahlem, M. Peterek, and R. Schmitt, “A Digital Perspective on Machine Tool Calibration,” Int. J. Automation Technol., Vol.14 No.3, pp. 360-368, 2020.
Data files:
  1. [1] A. A. Potdar, A. P. Longstaff, S. Fletcher, and N. S. Mian, “Application of Multi Sensor Data Fusion Based on Principal Component Analysis and Artificial Neural Network for Machine Tool Thermal Monitoring,” Proc. of Laser Metrology and Machine Performance XI: 11th Int. Conf. and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance (Lamdamap 2015), University of Huddersfield, Queensgate, Huddersfield, West Yorkshire, UK, pp. 228-237, 2015.
  2. [2] A. M. Abdulshahed, A. P. Longstaff, S. Fletcher, and A. Myers, “Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera,” Applied Mathematical Modelling, Vol.39, No.7, pp. 1837-1852, 2015.
  3. [3] C. Baum, C. Brecher, M. Klatte, T. H. Lee, and F. Tzanetos, “Thermally induced volumetric error compensation by means of integral deformation sensors,” Procedia CIRP, Vol.72, pp. 1148-1153, 2018.
  4. [4] K. Szipka, A. Archenti, G. W. Vogl, and M. A. Donmez, “Identification of machine tool squareness errors via inertial measurements,” CIRP Annals, Vol.68, No.1, pp. 547-550, 2019.
  5. [5] G. W. Vogl, M. A. Donmez, and A. Archenti, “Diagnostics for geometric performance of machine tool linear axes,” CIRP Annals – Manufacturing Technology, Vol.65, No.1, pp. 377-380, 2016.
  6. [6] M. Armendia, A. Alzaga, F. Peysson, T. Fuertjes, F. Cugnon, E. Ozturk, and D. Flum, “Machine Tool: From the Digital Twin to the Cyber-Physical Systems,” M. Armendia, M. Ghassempouri, E. Ozturk, and F. Peysson (Eds.), “Twin-Control: A Digital Twin Approach to Improve Machine Tools Lifecycle,” Springer Int. Publishing, pp. 3-21, 2019.
  7. [7] H. Schwenke W. Knapp, H. Haitjema, A. Weckenmann, R. Schmitt, and F. Delbressine, “Geometric error measurement and compensation of machines – An update,” CIRP Annals – Manufacturing Technology, Vol.57, No.2, pp. 660-675, 2008.
  8. [8] S. Ibaraki and W. Knapp, “Indirect Measurement of Volumetric Accuracy for Three-Axis and Five-Axis Machine Tools: A Review,” Int. J. Automation Technol., Vol.6, No.2, pp. 110-124, 2012.
  9. [9] B. Montavon, P. Dahlem, and S. H. Schmitt, “Fast Machine Tool Calibration using a single Laser Tracker,” Proc. of 13th Int. Conf. and Exhibition on Laser Metrology, Coordinate Measuring Machine, and Machine Tool Performance, pp. 203-213, 2019.
  10. [10] S. Aguado, D. Samper, J. Santolaria, and J. J. Aguilar, “Volumetric verification of multiaxis machine tool using laser tracker,” The Scientific World J., Vol.2014, 959510, 2014.
  11. [11] P. Freeman, “A Novel Means of Software Compensation for Robots and Machine Tools,” SAE Technical Paper, 2006-01-3167, 2006.
  12. [12] K. Umetsu, R. Furutnani, S. Osawa, T. Takatsuji, and T. Kurosawa, “Geometric calibration of a coordinate measuring machine using a laser tracking system,” Meas. Sci. Technol, Vol.16, No.12, pp. 2466-2472, 2005.
  13. [13] U. Mutilba, J. A. Yagüe-Fabra, E. Gomez-Acedo, G. Kortaberria, and A. Olarra, “Integrated multilateration for machine tool automatic verification,” CIRP Annals – Manufacturing Technology, Vol.67, No.1, pp. 555-558, 2018.
  14. [14] P. Dahlem, B. Montavon, M. Peterek, and R. H. Schmitt, “Enhancing Laser Step Diagonal Measurement by Multiple Sensors for Fast Machine Tool Calibration,” J. of Machine Engineering, Vol.18, No.2, pp. 64-74, 2018.
  15. [15] C. Wang, “Laser vector measurement technique for the determination and compensation of volumetric positioning errors. Part I: Basic theory,” Rev. Sci. Instrum, Vol.71, No.10, 3933, 2000.
  16. [16] N. Alami Mchichi and J. R. R. Mayer, “Axis Location Errors and Error Motions Calibration for a Five-axis Machine Tool Using the SAMBA Method,” Procedia CIRP, Vol.14, pp. 305-310, 2014.
  17. [17] C. Brecher, J. Behrens, M. Klatte, T. H. Lee, and F. Tzanetos, “Measurement and analysis of thermo-elastic deviation of five-axis machine tool using dynamic R-test,” Procedia CIRP, Vol.77, pp. 521-524, 2018.
  18. [18] U. Mutilba, E. Gomez-Acedo, G. Kortaberria, A. Olarra, and J. A. Yagüe-Fabra, “Traceability of On-Machine Tool Measurement: A Review,” Sensors (Basel, Switzerland), Vol.17, No.7, 1605, 2017.
  19. [19] Y. T. Chen, W. C. Lin, and C. S. Liu, “Design and experimental verification of novel six-degree-of freedom geometric error measurement system for linear stage,” Optics and Lasers in Engineering, Vol.92, pp. 94-104, 2017.
  20. [20] X. Li, W. Gao, H. Muto, Y. Shimizu, S. Ito, and S. Dian, “A six-degree-of-freedom surface encoder for precision positioning of a planar motion stage,” Precision Engineering, Vol.37, No.3, pp. 771-781, 2013.
  21. [21] Z. Gao, J. Hu, Y. Zhu, and G. Duan, “A new 6-degree-of-freedom measurement method of X-Y stages based on additional information,” Precision Engineering, Vol.37, No.3, pp. 606-620, 2013.
  22. [22] B. Montavon, P. Dahlem, M. Peterek, M. Ohlenforst, and R. H. Schmitt, “Modelling Machine Tools using Structure Integrated Sensors for Fast Calibration,” J. Manuf. Mater. Process., Vol.2, No.1, p. 14, 2018.
  23. [23] J. Mayr, J. Jedrzejewski, E. Uhlmann, M. A. Donmez, W. Knapp, F. Härtig, K. Wendt, R. Moriwaki, P. Shore, R. Schmitt, C. Brecher, T. Würz, and K. Wegener, “Thermal issues in machine tools,” CIRP Annals – Manufacturing Technology, Vol.61, No.2, pp. 771-791, 2012.
  24. [24] J. Mayr, “Beurteilung und Kompensation des Temperaturganges von Werkzeugmaschinen,” ETH Zurich, 2009.
  25. [25] VDW – German Machine Tool Builders, “Association, umati: universal machine tool interface.” [Accessed on March 2, 2020]
  26. [26] J. Pennekamp, R. Glebke, M. Henze, T. Meisen, C. Quix, R. Hai, L. Gleim, P. Niemietz, M. Rudack, S. Knape, A. Epple, D. Trauth, U. Vroomen, T. Bergs, C. Brecher, A. Buhrig-Polaczek, M. Jarke, and K. Wehrle, “Towards an Infrastructure Enabling the Internet of Production,” 2019 IEEE Int. Conf. on Industrial Cyber Physical Systems (ICPS), pp. 31-37, 2019.
  27. [27] C. Brecher, F. Klocke, R. Schmitt, and G. Schuh (Eds.) “Internet of Production für agile Unternehmen: AWK Aachener Werkzeugmaschinen-Kolloquium 2017, 18. bis 19. Mai (1st ed.),” Apprimus Verlag, 2017.
  28. [28] B. Montavon, M. Peterek, and R. H. Schmitt, “Model-based interfacing of large-scale metrology instruments,” Proc. of SPIE, 11059-11, 2019.
  29. [29] S. G. Hackel, F. Härtig, J. Hornig, and T. Wiedenhöfer, “The Digital Calibration Certificate,” PTB-Mitteilungen, Vol.127, Issue 4, pp. 75-81, 2017.
  30. [30] Y. Lin and Y. Shen, “Modelling of Five-Axis Machine Tool Metrology Models Using the Matrix Summation Approach,” The Int. J. of Advanced Manufacturing Technology, Vol.21, No.4, pp. 243-248, 2003.
  31. [31] C. Brecher, T. H. Lee, F. Tzanetos, and D. Zontar, “Hybrid modeling of thermo-elastic behavior of a three-axis machining center using integral deformation sensors,” Procedia CIRP, Vol.81, pp. 1301-1306, 2019.
  32. [32] J. Mayr, P. Blaser, A. Ryser, and P. Hernandez-Becerro, “An adaptive self-learning compensation approach for thermal errors on 5-axis machine tools handling an arbitrary set of sample rates,” CIRP Annals, Vol.67, No.1, pp. 551-554, 2018.
  33. [33] B. Montavon, P. Dahlem, and R. H. Schmitt, “Effektive Analyse von Werkzeugmaschinenkalibierdaten: VoluSoft als Beispiel für die anwenderfreundliche Visualisierung und erweiterte Ana-lyse von Werkzeugmaschinenkalibrierdaten,” wt Werkstattstechnik online, (11/12):n.n, 2018.
  34. [34] ISO 230-1:2012, “Test code for machine tools – Part 1: Geometric accuracy of machines operating under no-load or quasi-static conditions,” 2017.
  35. [35] M. Ohlenforst, P. Dahlem, M. Peterek, and R. Schmitt, “Geometriemessungen auf Werkzeugmaschinen: Vorgehen zum Bestimmen und Minimieren der Messunsicherheit,” wt Werkstattstechnik online, Vol.106. Nos.11-12, pp. 782-786, 2016.
  36. [36] M. Givi and J. R. R. Mayer, “Volumetric error formulation and mismatch test for five-axis CNC machine compensation using differential kinematics and ephemeral G-code,” The Int. J. of Advanced Manufacturing Technology, Vol.77, Nos.9-12, pp. 1645-1653, 2015.

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