Create a digital twin of the compressor impeller assembly process

Cover Page

Cite item

Full Text

Abstract

A digital dual compressor wheel assembly process has been developed to predict the angular rotations of the blade assembly rotating as it rotates. The calculated angles characterize the tension in the joints of the end flanges of the blades. At the input of a digital double sequence of data on geometry deviations with control of operations on parts. During the implementation, the random forest method was used, the study of which was carried out on a set of calculated experiments performed in the ANSYS environment using the element model of the assembled impeller. The experimental results show that the calculation error does not exceed 15 minutes of arc.

About the authors

Ekaterina Yu. Pechenina

Samara National Research University

Author for correspondence.
Email: ek-ko@list.ru

Graduate Student, Assistant Lecturer at the Engine Manufacturing Technologies

Russian Federation, Samara

References

  1. Inozemtsev A.A. Osnovy konstruirovaniya aviatsionnykh dvigateley i energeticheskikh ustanovok [Fundamentals of designing aircraft engines and power plants] / A.A. Inozemtsev, M.A. Nikhamkin, V.L. Sandratskiy. – M.: Mashinostroyeniye. 2008. – Vol.2. – 365 p. (in Russ.).
  2. Nepomiluev V.V., Djupin I.V. Povyshenie kachestva sborki putem obespechenija statisticheskoj upravljaemosti tehnologicheskih processov izgotovleknija detalej [Improving assembly quality by providing statistical controllability of technological processes for manufacturing parts]. Sborka v mashinostroenii, priborostroenii [Assembly in mechanical engineering, instrument making] / V.V. Nepomiluev, I.V. Djupin. – 2008. – No. 2. – Pp. 3-7. (in Russ.).
  3. Kannan S.M., Asha A., Jayabalan V. A new method in selective assembly to minimize clearance variation for a radial assembly using genetic algorithm. Quality engineering, 2005. Vol. 17. No. 4. Pp. 595-607. doi: 10.1080/08982110500225398.
  4. Samper S., Adragna P-A., Favreliere H., Pillet M. Modeling of 2D and 3D assemblies taking into account form errors of plane surfaces. J Comput Inf Sci Eng, 2009. Vol. 9. No. 2. Pp. 1-12. doi: 10.1115/1.3249575.
  5. Vezzetti E. Computer aided inspection: design of customer-oriented benchmark for noncontact 3D scanner evaluation. The International Journal of Advanced Manufacturing Technology, 2009. No. 41. Pp. 1140-1151. doi: 10.1007/s00170-008-1562-x.
  6. Chang H.-C., Li A.C. Automatic inspection of turbine blades using a 3-axis CMM together with a 2-axis dividing head. The International Journal of Advanced Manufacturing Technology, 2005. No.26. Pp. 789-796. doi: 10.1007/s00170-003-1877-6.
  7. Savio E., Chiffre L. De., Schmitt R. Metrology of freeform shaped parts // CIRP Annals – Manufacturing Technology. 2007. Vol. 56, No. 2, P. 810-835. doi: 10.1016/j.cirp.2007.10.008.
  8. Groch D., Poniatowska M. Simulation tests of the accuracy of fi tting two freeform. International Journal of Precision Engineering and Manufacturing, 2019. Vol. 21. Pp. 23-30. DOI: 0.1007/s12541-019-00252-4.
  9. Zhang Z., Zhang Z., Jin X., Zhang Q. A novel modelling method of geometric errors for precision assembly. The International Journal of Advanced Manufacturing Technology, 2018. Vol. 94. Pp. 1139–1160. doi: 10.1007/s00170-017-0936-3.
  10. Nepomiluev V.V., Majorova E.A. Optimizacija metoda individual’nogo podbora dlja mnogozvennyh razmernyh cepej [Optimization of the individual selection method for multi-link dimensional chains] / V.V. Nepomiluev, E.A. Majorova Izvestija MGTU «MAMI» [News of MGTU «MAMI»]. – 2008. – Vol. 2. – No.6. – Pp. 302-309.
  11. Osipovich D.A., Yarushin S.G., Makeyev A.B. Issledovaniye algoritmov podbora lopatok pri sborke soplovykh apparatov gazoturbinnogo dvigatelya [Investigation of the algorithms for the selection of blades during the assembly of gas turbine engine nozzles] / D.A. Osipovich, S.G. Yarushin. Makeyev A.B. – Sborka v mashinostroyenii, priborostroyenii [Assembly in mechanical engineering, instrument making]. – 2018. – No. 7 (216). – Pp. 313-319.
  12. Breiman L. Random Forests. Machine Learning, 2001. Vol. 45. No. 1. Pp. 5-32. doi: 10.1023/A:1010933404324.
  13. Cristianini N., Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, 2000. doi: 10.1017/CBO9780511801389.
  14. Murphy K. P. Machine Learning: A Probabilistic Perspective. The MIT Press. 2012. Сhapter 14.4.3, Pp. 492-493.

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2023 Pechenina E.Y.

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies