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

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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.

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References

  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. Campatelli G, Scippa A (2012) . Procedia CIRP 1:563. https://doi.org/10.1016/j.procir.2012.04.100

    Article  Google Scholar 

  3. Ehmann KF, Kapoor S, DeVor RE, Lazoglu I (1997) . J Manuf Sci Eng 119(4):655. https://doi.org/10.1115/1.2836805

    Article  Google Scholar 

  4. Kienzle O (1952) In: VDI-Z. VDI, Verlag, vol 96, pp 299–305

  5. Altintas Y, Lee P (1996) CIRP. Ann - Manuf Technol 45(1):59. https://doi.org/10.1016/S0007-8506(07)63017-0

    Article  Google Scholar 

  6. Grossi N (2017) . Int J Precis Eng Manuf 18(8):1173. https://doi.org/10.1007/s12541-017-0137-x

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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. 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

    Article  Google Scholar 

  11. Wan M, Zhang WH (2009) . Int J Mach Tools Manuf 49(5):424. https://doi.org/10.1016/j.ijmachtools.2008.12.004

    Article  Google Scholar 

  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. Matsumura T, Tamura S (2017) . Proced CIRP 58:566. https://doi.org/10.1016/j.procir.2017.03.268

    Article  Google Scholar 

  14. Wojciechowski S (2015) . Int J Mach Tools Manuf 89:110. https://doi.org/10.1016/j.ijmachtools.2014.10.006

    Article  Google Scholar 

  15. Budak E, Altintas Y, Armarego EJA (1996) . J Manuf Sci Eng 118 (2):216. https://doi.org/10.1115/1.2831014

    Article  Google Scholar 

  16. Karandikar JM, Schmitz TL, Abbas AE (2014) . J Manuf Sci Eng 136(2):021017. https://doi.org/10.1115/1.4026365

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  20. König W, Essel K, Witte L (1982) Verein Deutscher Eisenhüttenleute, Spezifische Schnittkraftwerte für die Zerspanung metallischer Werkstoffe. Verlag Stahleisen, Düsseldorf. OCLC: 64323901

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  24. Nelder JA, Mead R (1965) . Comput J 7 (4):308. https://doi.org/10.1093/comjnl/7.4.308

    Article  MathSciNet  Google Scholar 

  25. Lagarias JC, Reeds JA, Wright MH, Wright PE (1998) . SIAM J Optim 9(1):112

    Article  Google Scholar 

  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. 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. Coleman TF, Li Y (1996) . SIAM J Optim 6(4):1040. https://doi.org/10.1137/S1052623494240456

    Article  MathSciNet  Google Scholar 

  29. Coleman TF, Li Y (1994) . Math Program 67(1):189. https://doi.org/10.1007/BF01582221

    Article  Google Scholar 

  30. Rubeo MA, Schmitz TL (2016) . Precis Eng 45:311. https://doi.org/10.1016/j.precisioneng.2016.03.008

    Article  Google Scholar 

  31. Rinnooy Kan AHG, Timmer GT (1987) . Math Program 39(1):27. https://doi.org/10.1007/BF02592070

    Article  Google Scholar 

  32. Jones DR, Perttunen CD, Stuckman BE (1993) . J Optim Theory Appl 79 (1):157. https://doi.org/10.1007/BF00941892

    Article  MathSciNet  Google Scholar 

  33. Gablonsky JM, Kelley CT (2001) . J Glob Optim 21(1):27. https://doi.org/10.1023/A:1017930332101

    Article  Google Scholar 

  34. Price WL (1983) . J Optim Theory Appl 40(3):333. https://doi.org/10.1007/BF00933504

    Article  MathSciNet  Google Scholar 

  35. Kaelo P, Ali MM (2006) . J Optim Theory Appl 130(2):253. https://doi.org/10.1007/s10957-006-9101-0

    Article  MathSciNet  Google Scholar 

  36. Dhupia J, Girsang I (2012) . Mach Sci Technol 16(2):287. https://doi.org/10.1080/10910344.2012.673978

    Article  Google Scholar 

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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.

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Authors and Affiliations

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

    Max Schwenzer, Thomas Auerbach, Benjamin Döbbeler & Thomas Bergs

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  1. Max Schwenzer
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  2. Thomas Auerbach
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  3. Benjamin Döbbeler
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  4. Thomas Bergs
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Correspondence to Max Schwenzer.

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Schwenzer, M., Auerbach, T., Döbbeler, B. et al. Comparative study on optimization algorithms for online identification of an instantaneous force model in milling. Int J Adv Manuf Technol 101, 2249–2257 (2019). https://doi.org/10.1007/s00170-018-3109-0

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