Skip to main content

Advertisement

Springer Nature Link
Log in

An effective and automatic approach for parameters optimization of complex end milling process based on virtual machining

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

The demand for optimization of manufacturing processes rises as a reflection of the highly competitive market environment that requires shorter lead time and lower production costs. Although some approaches to milling process optimization have been developed based on analytical model using average cutting parameters, they are not available for complex workpieces when cutting parameters are time-varying and instantaneous cutting conditions need to be considered. In order to automate the optimization process and avoid costly machining tests, in this paper, an effective approach for parameters optimization of complex end milling process based on virtual machining is proposed. A computer-aided design (CAD)/computer-aided manufacturing (CAM) application is integrated for actual tool path generation and feedrate scheduling based on material removal rate. Then, a machining simulator based on octree and instantaneous force model is developed to evaluate feasibility of the given numerical control (NC) program, and the correctness of this simulator is verified by machining tests. The optimization process is controlled by the efficient global optimization method to find global optimal solution with fewer simulations and less computation time. During each iteration of the optimization process, NC programs are generated and evaluated automatically by the CAD/CAM application and the simulator, respectively. The effectiveness and efficiency of the proposed approach are proved by comparing the generated optimal solution (has reduced machining time and production cost) with the recommended cutting parameters from machining experts when machining an impeller.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Abbreviations

\( a_{p} \) :

Axial depth of cut (mm)

\( a_{r} \) :

Radial depth of cut (mm)

\( C_{production} \) :

Production cost

\( C_{s} ,C_{m} ,C_{c} ,C_{t} \) :

Set-up time cost, actual machining cost, tool changing time cost and tool wear cost

\( C_{T} ,C_{x} ,C_{y} ,C_{z} \) :

Coefficients of the tool life model

\( ds \) :

Contact length of the segment cutting edge

\( dF_{t} ,dF_{r} ,dF_{a} \) :

The differential tangential, radial and axial cutting forces

\( dF_{x} ,dF_{y} ,dF_{z} \) :

The differential x, y and z cutting force

\( dz \) :

Height of the axial element of the sliced cutter

\( f_{z} \) :

Feed per tooth (mm/tooth)

\( F\left( {x,y,z} \right) \) :

Implicit function

\( h_{cy} ,h_{uc} ,h_{ct} ,h_{lc} \) :

The different section heights of the automatically programmed tool (APT) cutter

\( h_{j} \left( {\varphi ,z} \right) \) :

Uncut chip thickness of the segment cutting edge

\( H \) :

Total machining depth in one process (mm)

\( j \) :

Flute number

\( k_{l} ,k_{t} \) :

The labor and tool cost factors

\( K_{tc} ,K_{rc} ,K_{ac} \) :

The tangential, radial and axial shear force coefficients

\( K_{te} ,K_{re} ,K_{ae} \) :

The tangential, radial and axial edge force coefficients

\( n \) :

Spindle speed (rpm)

\( N_{p} \) :

Milling pass number

\( t_{s} ,t_{m} ,t_{c} \) :

The set-up time, machining time and tool-change time

\( T \) :

Tool life

\( V \) :

Cutting speed (m/min)

\( VB_{ \hbox{max} } \) :

Maximum allowable tool wear

\( {\mathbf{x}} \) :

The design variable vector

\( \hat{y} \) :

The kriging model

\( \kappa \) :

Axial immersion angle

\( \varphi \) :

Radial immersion angle

\( \varphi_{st} ,\varphi_{ex} \) :

Radial entry and exit angles

\( \alpha ,\beta ,f,R,r,h,D \) :

Parameters to define the geometry of an APT cutter

\( \mu \left( {\mathbf{x}} \right),\varepsilon \left( {\mathbf{x}} \right) \) :

The regression and Gaussian process of the kriging model

\( \mu ,\sigma ,\theta ,p \) :

Parameters for kriging model

\( {\varvec{\Omega}} \) :

Design space

\( \Delta_{tol} \) :

Convergence tolerance

\( {\text{corr}}\left[ { \cdot , \cdot } \right] \) :

The correlation function

\( \Phi \left( \cdot \right),\phi \left( \cdot \right) \) :

Standard normal distribution and density function

References

  • Aggarwal, S., & Xirouchakis, P. (2013). Selection of optimal cutting conditions for pocket milling using genetic algorithm. The International Journal of Advanced Manufacturing Technology,66, 1943–1958.

    Article  Google Scholar 

  • Alajmi, M. S., Alfares, F. S., & Alfares, M. S. (2019). Selection of optimal conditions in the surface grinding process using the quantum based optimisation method. Journal of Intelligent Manufacturing,30, 1469–1481.

    Article  Google Scholar 

  • Altintas, Y. (2012). Manufacturing automation: Metal cutting mechanics, machine tool vibrations, and CNC design. Cambridge: Cambridge University Press.

    Google Scholar 

  • Altintas, Y., Kersting, P., Biermann, D., Budak, E., Denkena, B., & Lazoglu, I. (2014). Virtual process systems for part machining operations. CIRP Annals,63, 585–605.

    Article  Google Scholar 

  • Bharathi Raja, S., & Baskar, N. (2012). Application of Particle Swarm Optimization technique for achieving desired milled surface roughness in minimum machining time. Expert Systems with Applications,39, 5982–5989.

    Article  Google Scholar 

  • Bloomenthal, J., & Wyvill, B. (Eds.). (1997). Introduction to implicit surfaces. San Francisco, CA: Morgan Kaufmann Publishers Inc.

    Google Scholar 

  • Budak, E., Altintaş, Y., & Armarego, E. J. A. (1996). Prediction of milling force coefficients from orthogonal cutting data. Journal of Manufacturing Science and Engineering,118, 216.

    Article  Google Scholar 

  • Çiçek, A., Kıvak, T., & Ekici, E. (2015). Optimization of drilling parameters using Taguchi technique and response surface methodology (RSM) in drilling of AISI 304 steel with cryogenically treated HSS drills. Journal of Intelligent Manufacturing,26, 295–305.

    Article  Google Scholar 

  • Corso, L. L., Zeilmann, R. P., Nicola, G. L., Missell, F. P., & Gomes, H. M. (2013). Using optimization procedures to minimize machining time while maintaining surface quality. The International Journal of Advanced Manufacturing Technology,65, 1659–1667.

    Article  Google Scholar 

  • El-Mounayri, H., & Deng, H. (2010). A generic and innovative approach for integrated simulation and optimisation of end milling using solid modelling and neural network. International Journal of Computer Integrated Manufacturing,23, 40–60.

    Article  Google Scholar 

  • Ferry, W., & Yip-Hoi, D. (2008). Cutter-workpiece engagement calculations by parallel slicing for five-axis flank milling of jet engine impellers. Journal of Manufacturing Science and Engineering,130, 51011.

    Article  Google Scholar 

  • Fountas, N. A., Benhadj-Djilali, R., Stergiou, C. I., & Vaxevanidis, N. M. (2017). An integrated framework for optimizing sculptured surface CNC tool paths based on direct software object evaluation and viral intelligence. Journal of Intelligent Manufacturing,46, 811.

    Google Scholar 

  • Fountas, N. A., Vaxevanidis, N. M., Stergiou, C. I., & Benhadj-Djilali, R. (2014). Development of a software-automated intelligent sculptured surface machining optimization environment. The International Journal of Advanced Manufacturing Technology,75, 909–931.

    Article  Google Scholar 

  • Gao, L., Huang, J., & Li, X. (2012). An effective cellular particle swarm optimization for parameters optimization of a multi-pass milling process. Applied Soft Computing,12, 3490–3499.

    Article  Google Scholar 

  • Ginta, T. L., Amin, A., Radzi, H., & Lajis, M. A. (2009). Tool life prediction by response surface methodology in end milling titanium alloy Ti–6Al–4 V using uncoated WC–Co inserts. European Journal of Scientific Research,28(4), 533–541.

    Google Scholar 

  • Jones, D. R., Schonlau, M., & Welch, W. J. (1998). Efficient global optimization of expensive black-box functions. Journal of Global Optimization,13(4), 455–492.

    Article  Google Scholar 

  • Joy, J., & Feng, H.-Y. (2017). Frame-sliced voxel representation: An accurate and memory-efficient modeling method for workpiece geometry in machining simulation. Computer-Aided Design,88, 1–13.

    Article  Google Scholar 

  • Karunakaran, K. P., Shringi, R., Ramamurthi, D., & Hariharan, C. (2010). Octree-based NC simulation system for optimization of feed rate in milling using instantaneous force model. The International Journal of Advanced Manufacturing Technology,46, 465–490.

    Article  Google Scholar 

  • Kondayya, D., & Krishna, A. G. (2012). An integrated evolutionary approach for modelling and optimisation of CNC end milling process. International Journal of Computer Integrated Manufacturing,25, 1069–1084.

    Article  Google Scholar 

  • Kurt, M., & Bagci, E. (2011). Feedrate optimisation/scheduling on sculptured surface machining: A comprehensive review, applications and future directions. The International Journal of Advanced Manufacturing Technology,55, 1037–1067.

    Article  Google Scholar 

  • Lee, H. U., & Cho, D.-W. (2003). An intelligent feedrate scheduling based on virtual machining. The International Journal of Advanced Manufacturing Technology,22, 873–882.

    Article  Google Scholar 

  • Li, C., Li, L., Tang, Y., Zhu, Y., & Li, L. (2019). A comprehensive approach to parameters optimization of energy-aware CNC milling. Journal of Intelligent Manufacturing,30, 123–138.

    Article  Google Scholar 

  • Li, L., Liu, F., Chen, B., & Li, C. B. (2015). Multi-objective optimization of cutting parameters in sculptured parts machining based on neural network. Journal of Intelligent Manufacturing,26, 891–898.

    Article  Google Scholar 

  • Lu, K., Jing, M., Zhang, X., Dong, G., & Liu, H. (2015). An effective optimization algorithm for multipass turning of flexible workpieces. Journal of Intelligent Manufacturing,26, 831–840.

    Article  Google Scholar 

  • Merdol, S. D., & Altintas, Y. (2008). Virtual cutting and optimization of three-axis milling processes. International Journal of Machine Tools and Manufacture,48, 1063–1071.

    Article  Google Scholar 

  • Palanisamy, P., Rajendran, I., & Shanmugasundaram, S. (2007). Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations. The International Journal of Advanced Manufacturing Technology,32, 644–655.

    Article  Google Scholar 

  • Sacks, J., Welch, W. J., Mitchell, T. J., & Wynn, H. P. (1989). Design and analysis of computer experiments. Statistical Science,4(4), 409–423.

    Article  Google Scholar 

  • Silva, J. A., Abellán-Nebot, J. V., Siller, H. R., & Guedea-Elizalde, F. (2014). Adaptive control optimisation system for minimising production cost in hard milling operations. International Journal of Computer Integrated Manufacturing,27, 348–360.

    Article  Google Scholar 

  • Sortino, M., Belfio, S., & Totis, G. (2015). An innovative approach for automatic generation, verification and optimization of part programs in turning. Journal of Manufacturing Systems,36, 168–181.

    Article  Google Scholar 

  • Tandon, V., El-Mounayri, H., & Kishawy, H. (2002). NC end milling optimization using evolutionary computation. International Journal of Machine Tools and Manufacture,42, 595–605.

    Article  Google Scholar 

  • Tolouei-Rad, M., & Bidhendi, I. M. (1997). On the optimization of machining parameters for milling operations. International Journal of Machine Tools and Manufacture,37(1), 1–16.

    Article  Google Scholar 

  • Yan, J., & Li, L. (2013). Multi-objective optimization of milling parameters—the trade-offs between energy, production rate and cutting quality. Journal of Cleaner Production,52, 462–471.

    Article  Google Scholar 

  • Yusup, N., Sarkheyli, A., Zain, A. M., Hashim, S. Z. M., & Ithnin, N. (2014). Estimation of optimal machining control parameters using artificial bee colony. Journal of Intelligent Manufacturing,25, 1463–1472.

    Article  Google Scholar 

  • Yusup, N., Zain, A. M., & Hashim, S. Z. M. (2012). Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007–2011). Expert Systems with Applications,39, 9909–9927.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Science and Technology Major Project China Under Grant No. 2017ZX04016001.

Author information

Authors and Affiliations

  1. National Engineering Research Center of Die and Mold CAD, Shanghai Jiao Tong University, 1954 Hua Shan Road, Shanghai, 200030, China

    Hengyuan Ma, Wei Liu, Xionghui Zhou, Qiang Niu & Chuipin Kong

Authors
  1. Hengyuan Ma
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Wei Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Xionghui Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Qiang Niu
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Chuipin Kong
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding authors

Correspondence to Wei Liu or Xionghui Zhou.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, H., Liu, W., Zhou, X. et al. An effective and automatic approach for parameters optimization of complex end milling process based on virtual machining. J Intell Manuf 31, 967–984 (2020). https://doi.org/10.1007/s10845-019-01489-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-019-01489-6

Keywords