Skip to main content
SpringerLink
Log in

Enhanced method for the evaluation of the thermal impact of dry machining processes

  • Production Process
  • Published:
Production Engineering Aims and scope Submit manuscript

Abstract

From today’s point of view the modelling of machining operations is a promising tool to extend the productivity and the precision of future industrial manufacturing. The importance of predictive simulation and compensation of thermally induced workpiece deformation during machining is especially important in dry machining because of the absence of cooling lubricants. Since the simulation results mainly depend on the boundary conditions of the model, a detailed knowledge of them is necessary. In this case the most important boundary condition is the intensity and possibly the distribution of the surface heat flux representing the heat flow into the workpiece resulting from the chip formation. The surface heat flux cannot be measured directly. One possible way to determine surface heat fluxes is to employ a thermal model of the machining process and match simulated and measured time and space dependent temperature fields. This procedure is time-consuming and is in most cases subjective because the congruency of temperature fields is rated manually, e.g. by the position of single isotherms. Therefore an enhanced method for the determination of surface heat fluxes is proposed in this paper. The method is based on nonlinear optimisation techniques and a simple finite difference scheme for numerical solution of the heat equation (WORHP-FDM). The procedure is objective between repeat measurements and works in a fully automated manner. The implementation is validated by the comparison to an analytical solution of the moving heat source based on the model of Carslaw and Jaeger and then applied to measured thermal images from milling experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Abbreviations

T CJ (x, y) (°C):

Temperature field simulated with the model by Carslaw and Jaeger

\(\dot{q}_{CJ}\,(\hbox{W/mm}^2)\) :

Surface heat flux used in the model by Carslaw and Jaeger

v f  (mm/min):

Feed velocity/heat source velocity

f z  (mm):

Feed per tooth

\(\dot{q}_{WORHP}\,(\hbox{W/mm}^2)\) :

Surface heat flux identified by the WORHP-FDM approach

v c  (m/min):

Cutting velocity

a p  (mm):

Depth of cut

a e  (mm):

Width of cut

P el.meas  (W):

Measured spindle power

c p  (J/kg K):

Specific heat capacity

ρ (Mg/m3):

Mass density

k (W/m K):

Thermal conductivity

a (mm2/s):

Thermal diffusivity

T k i,j  (°C):

Discrete temperature identified by the WORHP-FDM approach

\(\bar{T}_{i,j}^k\,(^{\circ}\hbox{C})\) :

Discrete data temperature

g (°C/m):

Neumann boundary values

X :

Optimisation variable

FDM:

Finite difference method

NLP:

Nonlinear optimisation problem

SQP:

Sequential quadratic programming

NAND:

Nested analysis and design

SAND:

Simultaneous analysis and design

References

  1. Tönshoff HK (2003) Spanen, grundlagen. Springer, Berlin

    Google Scholar 

  2. Brinksmeier E, Sölter J (2009) Prediction of shape deviations in machining. CIRP Ann Manuf Technol 58:507–510

    Article  Google Scholar 

  3. Furness R, Sebastian A, Stoll A (2007) 100 Prozent MMS in der Getriebegehäuse-fertigung. Werkstatt und Betrieb 9:80–85

    Google Scholar 

  4. Müller-Hummel P (2000) Auf Kühlschmierstoffe verzichten. WB Werkstatt und Betrieb 133(6):76–79

    Google Scholar 

  5. Heisel U, Storchak M, Eberhard P, Gaugele T (2011) Experimental studies for verification of thermal effects in cutting. Prod Eng Res Devel 5:507–515. doi:10.1007/s11740-011-0312-3

    Article  Google Scholar 

  6. Loehe J, Zaeh MF, Roesch O (2009) In-process deformation measurement of thin-walled workpieces. Procedia CIRP 1:546–551

    Article  Google Scholar 

  7. Joliet R, Byfut A, Kersting P, Schröder A, Zabel A (2013) Validation of a heat input model for the prediction of thermomechanical deformations during NC milling. Procedia CIRP 8:403–408

    Article  Google Scholar 

  8. Klocke F, Lung D, Puls H (2013) FEM-modelling of the thermal workpiece deformation in dry turning. Procedia CIRP 8:240–245

    Article  Google Scholar 

  9. Puls H, Klocke F, Lung D (2012) A new experimental methodology to analyse the friction behaviour at the tool-chip interface in metal cutting. Prod Eng Res Devel 6:349–354. doi:10.1007/s11740-012-0386-6

    Article  Google Scholar 

  10. Schindler S, Zimmermann M, Aurich JC, Steinmann P (2013) Modeling deformations of the workpiece and removal of material when turning. Procedia CIRP 8:39–44

    Article  Google Scholar 

  11. Dyck M (2007) Beitrag zur analyse thermisch bedingter Werkstückdeformationen in Trockenbearbeitungsprozessen. Dr. Ing. Dissertation, WBK

  12. Pabst R (2008) Mathematische Modellierung der Wärmestromdichte zur Simulation des thermischen Bauteilverhaltens bei der Trockenbearbeitung. Dr. Ing. Dissertation, WBK

  13. Richardson DJ (2006) Modelling of cutting induced workpiece temperatures for dry milling. Int J Mach Tools Manuf 46:1139–1145

    Article  Google Scholar 

  14. Biermann D, Iovkov I, Blum H, Rademacher A, Taebi K, Suttmeier FT, Klein N (2012) Thermal aspects in deep hole drilling of aluminium cast alloy using twist drills and MQL. Procedia CIRP 3:245–250

    Article  Google Scholar 

  15. Biermann D, Iovkov I (2013) Thermal aspects in deep hole drilling of aluminium cast alloy using twist drills and MQL. Procedia CIRP 3:245–250

    Article  Google Scholar 

  16. Chen M (2003) Experimental research on the dynamic characteristics of the cutting temperature in the process of high-speed milling. J Mater Process Technol 138:468–471

    Article  Google Scholar 

  17. Pittala GM (2011) A new approach to the prediction of temperature of the workpiece of face milling operations of Ti-6Al-4V. Appl Therm Eng 31:173–180

    Article  Google Scholar 

  18. Sölter J, Gulpak M (2012) Heat partitioning in dry milling of steel. CIRP Ann Manuf Technol 61(1):87–90

    Article  Google Scholar 

  19. Büskens C, Wassel D (2013) The ESA NLP solver WORHP. Modeling and optimization in space engineering. In: J. D. Pintér (Hrsg.), Springer optimization and its applications, vol 73. Springer, Berlin

  20. Jaeger JC (1942) Moving sources of heat and the temperature at sliding contacts. Proc R Soc N S W 76/3:203–224

    Google Scholar 

  21. Carslaw H, Jaeger JC (1959) Conduction of heat in solids. Oxford Science Publications, Oxford University Press, Oxford

    Google Scholar 

  22. Marsal D (1989) Finite Differenzen und Elemente. Springer, Berlin

    Book  MATH  Google Scholar 

  23. Ludwig R (1971) Methoden der Fehler- und Ausgleichsrechnung 2nd edn. Deutscher Verlag der Wissenschaft, Berlin

    Google Scholar 

  24. Geiger C, Kanzow C (2002) Theorie und Numerik restringierter Optimierungsaufgaben. Springer, Berlin

    Book  MATH  Google Scholar 

  25. Arora JS, Wang Q (2005) Review of formulations for structural and mechanical system optimisation. Struct Multidiscip Optim 30(4):251–272

    Article  MATH  MathSciNet  Google Scholar 

  26. N.N. (1997) Taschenbuch der Stahl-Eisen-Werkstoffblätter. Verein Deutscher Eisenhüttenleute, Verlag Stahleisen GmbH, Düsseldorf

Download references

Acknowledgments

The results in this paper were obtained within the DFG priority programme 1480 “Modelling, Simulation and Compensation of Thermal Effects for Complex Machining Processes”. The authors thank the Deutsche Forschungsgemeinschaft (DFG) for the financial support.

Author information

Authors and Affiliations

  1. Optimisation and Optimal Control, Center for Industrial Mathematics (ZeTeM), MZH, Bibliothekstr. 1, 28359, Bremen, Germany

    Heinrich Wernsing & Christof Büskens

  2. Manufacturing Technologies, University of Bremen, Badgasteiner Str. 1, 28359, Bremen, Germany

    Maxim Gulpak, Jens Sölter & Ekkard Brinksmeier

Authors
  1. Heinrich Wernsing
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maxim Gulpak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christof Büskens
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jens Sölter
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ekkard Brinksmeier
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maxim Gulpak.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wernsing, H., Gulpak, M., Büskens, C. et al. Enhanced method for the evaluation of the thermal impact of dry machining processes. Prod. Eng. Res. Devel. 8, 291–300 (2014). https://doi.org/10.1007/s11740-013-0523-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11740-013-0523-x

Keywords

Search

Navigation