Neural Networks in Modeling of CNC Milling of Moderate Slope Surfaces

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 285)


Computer numerical control (CNC) allows achieving a high degree of automation of machine tools by pre-programmed numerical commands. CNC milling process is widely used in industry for machining of complex parts. The need of a description of the CNC milling process is necessary for production of precise parts. This paper introduces artificial neural network based modeling, while the CNC milling of moderate slope shapes is studied. The developed neural models consist of two inputs and two outputs. The created neural models were experimentally tested on the real data. Then, the evaluation and comparison of all models were performed.


Artificial neural networks CNC milling Modeling Surface quality 


  1. 1.
    Bouzakis, K.-D., Aichouh, P., Efstathiou, K.: Determination of the chip geometry, cutting force and roughness in free form surfaces finishing milling, with ball end tools. Int. J. Mach. Tools Manuf. 43, 499–514 (2003)CrossRefGoogle Scholar
  2. 2.
    Lukovics, I.: High speed milling of metal and polymer materials. Manuf. Technol. 4, 29–33 (2004)Google Scholar
  3. 3.
    Kasina, M., Vasilko, K.: Experimental verification of the relation between the surface roughness and the type of used tool coating. Manuf. Technol. 12, 27–30 (2012)Google Scholar
  4. 4.
    Miko, B., Beni B.: Study of surface roughness in case of Z-level finishing. In: International GTE Conference Manufacturing 2012, Budapest (2012)Google Scholar
  5. 5.
    Izol, P., Beno, J., Balazs, M.: Precision and surface roughness when free–form–surface milling. Manuf. Ind. Eng. 1, 70–73 (2011)Google Scholar
  6. 6.
    Sebelova, E., Chladil, J.: Tool wear and machinability of wood-based materials during machining process. Manuf. Technol. 13, 231–236 (2013)Google Scholar
  7. 7.
    Cerny, J., Ovsik, M., Bednarik, M., Mizera, A., Manas, D., Manas, M., Stanek, M.: Mod-ern methods of design of ergonomics parts. In: Recent Research Circuits Systems, vol. 7, pp. 321–324. Kos (2012)Google Scholar
  8. 8.
    Wu, C.-T.: Establishing a correlative model for improving NC machining process. Int. J. Mech. 5, 100–112 (2011)Google Scholar
  9. 9.
    Ghionea, I., Ghionea, A.: Simulation Techniques in CAD-CAM Processing by Milling of Surfaces on NC Machine-Tools. In: Advances in Production, Automation and Transportation Systems, vol. 1, pp. 135-140. Brasov (2013)Google Scholar
  10. 10.
    Dragoi, M.V.: Ball nose milling cutter radius compensation in Z axis for CNC. In: Proceedings of 8th WSEAS International Conference on Software Engineering Parallel and Distributed Systems, pp. 57–60. Cambridge (2009)Google Scholar
  11. 11.
    Zebala, W.: Milling optimization of difficult to machine alloys. Management 1, 59–70 (2010)Google Scholar
  12. 12.
    Zebala, W., Matras, A., Beno, J.: Optimization of free-form surface milling. Manuf. Eng. 3, 17–20 (2011)Google Scholar
  13. 13.
    Benardos, P.G., Vosniakos, G.C.: Predicting surface roughness in machining: a review. Int. J. Mach. Tool. Man. 43, 833–844 (2003)CrossRefGoogle Scholar
  14. 14.
    Cubonova, N.: Postprocessing of CL data in CAD/CAM system Edgecam using the constructor of postprocessors. Manuf. Technol. 13, 158–163 (2013)Google Scholar
  15. 15.
    Felho, C.: A method for calculation of theoretical roughness in face milling. In: Proceedings of 8th International Tools Conference, pp. 84–87. Zlin (2011)Google Scholar
  16. 16.
    Micietova, A., Neslusan, M., Cillikova, M.: Influence of surface geometry and structure after non-conventional methods of parting on the following milling operations. Manuf. Technol. 13, 199–204 (2013)Google Scholar
  17. 17.
    Quinsat, Y., Sabourin, L., Lartigue, C.: Surface topography in ball end milling process: description of a 3D surface roughness parameter. J. Mater. Process. Technol. 195, 135–143 (2008)CrossRefGoogle Scholar
  18. 18.
    Ho, W.-H., Tsai, J.-T., Lin, B.-T., Chou, J.-H.: Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm. Expert Syst. Appl. 36, 3216–3222 (2009)CrossRefGoogle Scholar
  19. 19.
    Buj-Corral, I., Vivancos-Calvet, J., Dominguez-Fernandez, A.: Surface topography in ball-end milling processes as a function of feed per tooth and radial depth of cut. Int. J. Mach. Tools Manuf. 53, 151–159 (2012)CrossRefGoogle Scholar
  20. 20.
    Dhokia, V.G., Kumar, S., Vichare, P., Newman, S.T.: An intelligent approach for the prediction of surface roughness in ball-end machining of polypropylene. Rob. Comput. Integr. Manuf. 24, 835–842 (2008)CrossRefGoogle Scholar
  21. 21.
    Felho, C., Kundrak, J.: Characterization of topography of cut surface based on theoretical roughness indexes. Key Eng. Mater. 496, 194–199 (2011)CrossRefGoogle Scholar
  22. 22.
    Antoniadis, A., Savakis, C., Bilalis, N., Balouktsis, A.: Prediction of surface topomorphy and roughness in ball-end milling. Int. J. Adv. Manuf. Technol. 21, 965–971 (2003)CrossRefGoogle Scholar
  23. 23.
    Jung, T.-S., Yang, M.-Y., Lee, K.-J.: A new approach to analysing machined surfaces by ball-end milling, part I. Int. J. Adv. Manuf. Technol. 25, 833–840 (2005)CrossRefGoogle Scholar
  24. 24.
    Quintana, G., Ciurana, J., Ribatallada, J.: Surface roughness generation and material removal rate in ball end milling operations. Mater. Manuf. Process. 25, 386–398 (2010)CrossRefGoogle Scholar
  25. 25.
    Tandon, V., El-Mounayri, H., Kishawy, H.: NC end milling optimization using evolutionary computation. Int. J. Mach. Tools Manuf. 42, 595–605 (2002)CrossRefGoogle Scholar
  26. 26.
    Iliescu, M., Spanu, P., Rosu, M., Comanescu, B.: Simulation of cylindrical-face milling and modeling of resulting surface roughness when machining polymeric composites. In: Proceedings of 11th WSEAS International Conference on Automatic Control, Modelling and Simulation, pp. 219–224. Istanbul (2009)Google Scholar
  27. 27.
    Folea, M., Schlegel, D., Lupulescu, N., Parv, L.: Modeling surface roughness in high speed milling: cobalt based superalloy case study. In: Proceedings of 1st International Conference on Manufacturing Engineering Quality Production System, pp. 353–357. Brasov (2009)Google Scholar
  28. 28.
    Babur, O., Oktem, H., Kurtaran, H.: Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm. Int. J. Adv. Manuf. Technol. 27, 234–241 (2005)CrossRefGoogle Scholar
  29. 29.
    Al-Zubaidi, S., Ghani, J.A., Haron, C.H.C.: Application of artificial neural networks in prediction tool life of PVD coated carbide when end milling of TI6aL4v alloy. Int. J. Mech. 6, 179–186 (2012)Google Scholar
  30. 30.
    Mankova, I., Vrabel, M., Kovac, P.: Artificial neural network application for surface roughness prediction when drilling nickel based alloy. Manuf. Technol. 13, 193–199 (2013)Google Scholar
  31. 31.
    Quintana, G., Garcia-Romeu, M.L., Ciurana, J.: Surface roughness monitoring application based on artificial neural networks for ball-end milling operations. J. Intell. Manuf. 22, 607–617 (2011)CrossRefGoogle Scholar
  32. 32.
    Yegnanarayana, B.: Artificial neural networks. PHI Learning Pvt. Ltd., New Delhi (2004)Google Scholar
  33. 33.
    Karayel, D.: Prediction and control of surface roughness in CNC lathe using artificial neural network. J. Mater. Process. Technol. 209, 3125–3137 (2009)CrossRefGoogle Scholar
  34. 34.
    Correa, M., Bielza, C., Pamies-Teixeira, J.: Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process. Expert Syst. Appl. 36, 7270–7279 (2009)CrossRefGoogle Scholar
  35. 35.
    Oktem, H., Erzurumlu, T., Erzincanli, F.: Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm. Mater. Des. 27, 735–744 (2006)CrossRefGoogle Scholar
  36. 36.
    Venkatesan, D., Kannan, K., Saravanan, R.: A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Comput. Appl. 18, 135–140 (2009)CrossRefGoogle Scholar
  37. 37.
    Zeroudi, N., Fontaine, M.: Prediction of machined surface geometry based on analytical modelling of ball-end milling. Procedia CIRP 1, 108–113 (2012)CrossRefGoogle Scholar
  38. 38.
    El-Mounayri, H., Kishawy, H., Briceno, J.: Optimization of CNC ball end milling: a neural network-based model. J. Mater. Process. Technol. 166, 50–62 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of TechnologyTomas Bata University in ZlinZlinCzech Republic

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