Using Local Coordinate Systems for Dimensional Analysis in the Machining

  • S. S. KugaevskiiEmail author
  • V. N. Ashikhmin
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


The article considers the implementation of the attached coordinate systems for machining at CNC machines. The main objective of this study is to suggest the improvements of processing accuracy. The problem arises when the machinable surface does not belong to a technological base. We offer the method based on preliminary measurement of workpiece dimensions. Further, the coordinate value enters into a CNC unit. The NC program uses the local coordinate systems, which are linked with measured dimensions of selected surfaces. In this case, the machining accuracy does not depend on errors connected with previous processing stages. Local coordinate systems can be effectively used for machining “rotary body”-type components at turning CNC machines or for machining conditions of box-like parts at milling CNC machines. We also offer the special device for adjustment “0” point outside a machine tool. As a result, the proposed method allows for a dimension chain shortening and accuracy improving.


Machining Dimensional analysis CNC machine Coordinate system Preliminary measurement 



The work was supported by Ministry of Education and Science of the Russian Federation, contract no. 02.G25.31.0148 with Sverdlovsk Instrumental Plant.


  1. 1.
    ISO 1101:2012 Geometrical product specifications (GPS)—geometrical tolerancing—tolerances of form, orientation, location and run-out. ISO, GenevaGoogle Scholar
  2. 2.
    Rao S (1992) Reliability based design. McGraw-Hill, New YorkGoogle Scholar
  3. 3.
    Meadows JD (1995) Geometric dimensioning and tolerancing. Marcel Dekker, New YorkGoogle Scholar
  4. 4.
    Colosimo BM, Senin N (2016) Geometric tolerances. Springer, London, Dordrecht, Heidelberg, New YorkGoogle Scholar
  5. 5.
    Tsvetkov VD (1979) Sistemno-strukturnoe modelirovanie i avtomatizatsia technologicheskich prozessov (System-structure modeling and automation of technological process designing). MinskGoogle Scholar
  6. 6.
    Cogorno G (2006) Geometric dimensioning and tolerancing for mechanical design. McGraw Hill, New YorkGoogle Scholar
  7. 7.
    Ashikhmin V, Kugaevskii S (2013) Dimensional analysis in the machining of housing components with cast holes. Russ Eng Res 33(9):509–513CrossRefGoogle Scholar
  8. 8.
    Ameta G, Moylan S, Witherell P, Lipman R (2015) Challenges in tolerance transfer for additive manufacturing. American Society for Precision Engineering, New York.
  9. 9.
    Huang M, Zhong Y (2006) Sequential design of optimum sized and geometric tolerances. In: Kordic V, Lazinica A, Merdan M (eds) Manufacturing the future: concepts, technologies and visions, ARS/plV, Germany, July 2006Google Scholar
  10. 10.
    Ashikmin V, Zarukaev V (2005) Razmernii analiz v technologicheskom proektirovanii (Dimension analysis of technology design). Tutorial, EkaterinburgGoogle Scholar
  11. 11.
    Ashikmin V, Zarukaev V (2007) Avtomatizaciya proektirovaniia technologicheskogo processa (Automaticaly technological process designing). Tutorial, EkaterinburgGoogle Scholar
  12. 12.
    Ashikmin VN (2010) Razmernii analiz tekhnologicheskich processov (Dimensional analysis of manufacturing process). Practicum, National Research Nuclear University, MoscowGoogle Scholar
  13. 13.
    Rao RV (2011) Advanced modeling and optimization of manufacturing processes. Springer-Verlag, LondonCrossRefGoogle Scholar
  14. 14.
    SINUMERIK 808D on PC V4.4 Ed.2
  15. 15.
    FANUC Series 0i-
  16. 16.
  17. 17.
  18. 18.
  19. 19.
  20. 20.
    Kugaevskii S (2012) Nekotorie aspekti effektivnosti primeneniia stankov tipa OC I fpezernich stankov s CYPU (Some aspects of the efficiency of the use multiaxial and milling CNC machines). Proc III Conf PLM-Syst Air Compon 1:126–131Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Ural Federal UniversityEkaterinburgRussia

Personalised recommendations