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Comparative study on optimization algorithms for online identification of an instantaneous force model in milling

  • Max SchwenzerEmail author
  • Thomas Auerbach
  • Benjamin Döbbeler
  • Thomas Bergs
ORIGINAL ARTICLE
  • 61 Downloads

Abstract

Mechanistic force models allow an estimation of the force components in cutting technology. This is essential for an accurate simulation of the force, an analysis of the tool load, or a model-based predictive force control. The model coefficients do not only depend on the material and the tool but also on the engagement condition. This requires an online identification of those coefficients. To determine those coefficients, this paper uses curve fitting based on the instantaneous uncut chip thickness, a method that has only recently gained momentum in milling. This work applies this technique to the well-established, nonlinear Kienzle force model. The paper reviews a broad range of nonlinear, derivative-free optimization algorithms for this least-squares curve-fitting problem evaluating accuracy and runtime. The evaluation is conducted on 121experiments with distinct process conditions resulting in a statistically verified conclusion. The results indicate that in spite of a heterogeneous optimization space, local constrained algorithms are most suited for model identification.

Keywords

Cutting force Model identification Instantaneous uncut chip thickness Cutting force coefficients Milling 

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Notes

Acknowledgements

The authors would like to thank the German Research Foundation (DFG) for the support of the depicted research within the Cluster of Excellence Integrative Production Technology for High-Wage Countries.

Furthermore, we would like to acknowledge the financial support of the Kopernikus-project “SynErgie”by the Federal Ministry of Education and Research (BMBF).

Selected preliminary results of this work were presented at the 14th International Conference on High Speed Machining 2018, San Sebastian, Spain.

References

  1. 1.
    Stemmler S, Abel D, Adams O, Klocke F (2016) 8th IFAC Conference on manufacturing modelling, management and control MIM 2016Troyes. France 49(12):11.  https://doi.org/10.1016/j.ifacol.2016.07.542 Google Scholar
  2. 2.
    Campatelli G, Scippa A (2012) . Procedia CIRP 1:563.  https://doi.org/10.1016/j.procir.2012.04.100 CrossRefGoogle Scholar
  3. 3.
    Ehmann KF, Kapoor S, DeVor RE, Lazoglu I (1997) . J Manuf Sci Eng 119(4):655.  https://doi.org/10.1115/1.2836805 CrossRefGoogle Scholar
  4. 4.
    Kienzle O (1952) In: VDI-Z. VDI, Verlag, vol 96, pp 299–305Google Scholar
  5. 5.
    Altintas Y, Lee P (1996) CIRP. Ann - Manuf Technol 45(1):59.  https://doi.org/10.1016/S0007-8506(07)63017-0 CrossRefGoogle Scholar
  6. 6.
    Grossi N (2017) . Int J Precis Eng Manuf 18(8):1173.  https://doi.org/10.1007/s12541-017-0137-x CrossRefGoogle Scholar
  7. 7.
    Wan M, Zhang WH, Tan G, Qin GH (2007) . Proc Inst Mech Eng Part B: J Eng Manuf 221(6):1007.  https://doi.org/10.1243/09544054JEM515 CrossRefGoogle Scholar
  8. 8.
    Wan M, Zhang WH, Dang JW, Yang Y (2009) . Int J Mach Tools Manuf 49(14):1144.  https://doi.org/10.1016/j.ijmachtools.2009.08.005 CrossRefGoogle Scholar
  9. 9.
    Wei ZC, Guo ML, Wang MJ, Li SQ, Liu SX (2018) The international journal of advanced manufacturing technology.  https://doi.org/10.1007/s00170-017-1380-0
  10. 10.
    Wang L, Si H, Guan L, Liu Z (2018) . Int J Adv Manuf Technol 94 (5):2961.  https://doi.org/10.1007/s00170-017-1086-3 CrossRefGoogle Scholar
  11. 11.
    Wan M, Zhang WH (2009) . Int J Mach Tools Manuf 49(5):424.  https://doi.org/10.1016/j.ijmachtools.2008.12.004 CrossRefGoogle Scholar
  12. 12.
    Auerbach T, Gierlings S, Veselovac D, Seidner R, Kamps S, Klocke F (2015) In: Proceedings of the ASME Turbo Expo: Turbine Technical Conference and Exposition 2015, ed. by A.S. of Mechanical Engineers. ASME, pp V006T21A006.  https://doi.org/10.1115/GT2015-43209
  13. 13.
    Matsumura T, Tamura S (2017) . Proced CIRP 58:566.  https://doi.org/10.1016/j.procir.2017.03.268 CrossRefGoogle Scholar
  14. 14.
    Wojciechowski S (2015) . Int J Mach Tools Manuf 89:110.  https://doi.org/10.1016/j.ijmachtools.2014.10.006 CrossRefGoogle Scholar
  15. 15.
    Budak E, Altintas Y, Armarego EJA (1996) . J Manuf Sci Eng 118 (2):216.  https://doi.org/10.1115/1.2831014 CrossRefGoogle Scholar
  16. 16.
    Karandikar JM, Schmitz TL, Abbas AE (2014) . J Manuf Sci Eng 136(2):021017.  https://doi.org/10.1115/1.4026365 CrossRefGoogle Scholar
  17. 17.
    Adem KAM, Fales R, El-Gizawy AS (2015) . Int J Adv Manuf Technol 79 (9):1671.  https://doi.org/10.1007/s00170-015-6935-3 CrossRefGoogle Scholar
  18. 18.
    Zhang D, Mo R, Chang Z, Sun H, Li C (2016) . Int J Adv Manuf Technol 84(1):621.  https://doi.org/10.1007/s00170-015-7707-9 CrossRefGoogle Scholar
  19. 19.
    Yao Q, Luo M, Zhang D, Wu B (2018) . Mech Syst Signal Process 103:39.  https://doi.org/10.1016/j.ymssp.2017.09.038 CrossRefGoogle Scholar
  20. 20.
    König W, Essel K, Witte L (1982) Verein Deutscher Eisenhüttenleute, Spezifische Schnittkraftwerte für die Zerspanung metallischer Werkstoffe. Verlag Stahleisen, Düsseldorf. OCLC: 64323901Google Scholar
  21. 21.
    Jayaram S, Kapoor S, DeVor R (2001) . Int J Mach Tools Manuf 41(2):265.  https://doi.org/10.1016/S0890-6955(00)00076-6 CrossRefGoogle Scholar
  22. 22.
    Zhang Z, Li H, Meng G, Ren S, Zhou J (2017) . Int J Adv Manuf Technol 89(5):1709.  https://doi.org/10.1007/s00170-016-9186-z CrossRefGoogle Scholar
  23. 23.
    Kline W, DeVor R (1983) . Int J Mach Tool Des Res 23(2):123.  https://doi.org/10.1016/0020-7357(83)90012-4 CrossRefGoogle Scholar
  24. 24.
    Nelder JA, Mead R (1965) . Comput J 7 (4):308.  https://doi.org/10.1093/comjnl/7.4.308 MathSciNetCrossRefGoogle Scholar
  25. 25.
    Lagarias JC, Reeds JA, Wright MH, Wright PE (1998) . SIAM J Optim 9(1):112CrossRefGoogle Scholar
  26. 26.
    Powell M (2004) The NEWUOA software for unconstrained optimization without derivatives. http://www.damtp.cam.ac.uk/user/na/NA_papers/NA2004_08.pdf.NA2004/08
  27. 27.
    Powell M (2009) The BOBYQA algorithm for bound constrained optimization without derivatives. http://www.damtp.cam.ac.uk/user/na/NA_papers/NA2009_06.pdf.DAMTP2009/NA06
  28. 28.
    Coleman TF, Li Y (1996) . SIAM J Optim 6(4):1040.  https://doi.org/10.1137/S1052623494240456 MathSciNetCrossRefGoogle Scholar
  29. 29.
    Coleman TF, Li Y (1994) . Math Program 67(1):189.  https://doi.org/10.1007/BF01582221 CrossRefGoogle Scholar
  30. 30.
    Rubeo MA, Schmitz TL (2016) . Precis Eng 45:311.  https://doi.org/10.1016/j.precisioneng.2016.03.008 CrossRefGoogle Scholar
  31. 31.
    Rinnooy Kan AHG, Timmer GT (1987) . Math Program 39(1):27.  https://doi.org/10.1007/BF02592070 CrossRefGoogle Scholar
  32. 32.
    Jones DR, Perttunen CD, Stuckman BE (1993) . J Optim Theory Appl 79 (1):157.  https://doi.org/10.1007/BF00941892 MathSciNetCrossRefGoogle Scholar
  33. 33.
    Gablonsky JM, Kelley CT (2001) . J Glob Optim 21(1):27.  https://doi.org/10.1023/A:1017930332101 CrossRefGoogle Scholar
  34. 34.
    Price WL (1983) . J Optim Theory Appl 40(3):333.  https://doi.org/10.1007/BF00933504 MathSciNetCrossRefGoogle Scholar
  35. 35.
    Kaelo P, Ali MM (2006) . J Optim Theory Appl 130(2):253.  https://doi.org/10.1007/s10957-006-9101-0 MathSciNetCrossRefGoogle Scholar
  36. 36.
    Dhupia J, Girsang I (2012) . Mach Sci Technol 16(2):287.  https://doi.org/10.1080/10910344.2012.673978 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratory for Machine Tools and Production Engineering (WZL)RWTH Aachen UniversityAachenGermany

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