Journal of Intelligent Manufacturing

, Volume 29, Issue 8, pp 1683–1693 | Cite as

A comparison of machine learning methods for cutting parameters prediction in high speed turning process

  • Zoran JurkovicEmail author
  • Goran Cukor
  • Miran Brezocnik
  • Tomislav Brajkovic


Support vector machines are arguably one of the most successful methods for data classification, but when using them in regression problems, literature suggests that their performance is no longer state-of-the-art. This paper compares performances of three machine learning methods for the prediction of independent output cutting parameters in a high speed turning process. Observed parameters were the surface roughness (Ra), cutting force \((F_{c})\), and tool lifetime (T). For the modelling, support vector regression (SVR), polynomial (quadratic) regression, and artificial neural network (ANN) were used. In this research, polynomial regression has outperformed SVR and ANN in the case of \(F_{c}\) and Ra prediction, while ANN had the best performance in the case of T, but also the worst performance in the case of \(F_{c}\) and Ra. The study has also shown that in SVR, the polynomial kernel has outperformed linear kernel and RBF kernel. In addition, there was no significant difference in performance between SVR and polynomial regression for prediction of all three output machining parameters.


Turning Roughness Cutting force Tool life ANN SVR 



This work was supported and funded by the University of Rijeka, Croatia, (OJ 212, MT 137), and Ministry of Science, Education and Sport of the Republic of Croatia under bilateral cooperation with University of Maribor, Slovenia.


  1. Abu-Mostafa, Y. S., Magdon-Ismail, M., & Lin, H. T. (2012). Learning from data. A short course.
  2. Aydin, M., Ucar, M., Cengiz, A., Kurt, M., & Bakir, B. (2014). A methodology for cutting force prediction in side milling. Materials and Manufacturing Processes. doi: 10.1080/10426914.2014.912315.CrossRefGoogle Scholar
  3. Bishop, C. M. (2005). Neural networks for pattern recognition. Oxford: Oxford University Press.Google Scholar
  4. Brezocnik, M., Kovacic, M., & Ficko, M. (2004). Prediction of surface roughness with genetic programming. Journal of Materials Processing Technology. doi: 10.1016/j.jmatprotec.2004.09.004.CrossRefGoogle Scholar
  5. Campbell, C., & Ying, Y. (2011). Learning with support vector machines. In Synthesis Lectures on Artificial Intelligence and Machine Learning. California: Morgan and Claypool Publishers. doi: 10.2200/S00324ED1V01Y201102AIM010.CrossRefGoogle Scholar
  6. Cho, S., Asfour, S., Onar, A., & Kaundinya, N. (2005). Tool breakage detection using support vector machine learning in a milling process. International Journal of Machine Tools and Manufacture,. doi: 10.1016/j.ijmachtools.2004.08.016.CrossRefGoogle Scholar
  7. Cukor, G., & Jurkovic, Z. (2010). Optimization of turning using evolutionary algorithms. Engineering Review, 30, 1–10.Google Scholar
  8. Cukor, G., Jurkovic, Z., & Sekulic, M. (2011). Rotatable central composite design of experiments versus Taguchi method in the optimization of turning. Metalurgija, 50, 17–20.Google Scholar
  9. Hrelja, M., Klancnik, S., Irgolic, T., Paulic, M., Jurkovic, Z., Balic, J., & Brezocnik, M. (2014). Particle swarm optimization approach for modelling a turning process. Advances in Production Engineering & Management. doi: 10.14743/apem2014.1.173.CrossRefGoogle Scholar
  10. Jurkovic, Z., Cukor, G., & Andrejcak, I. (2010). Improving the surface roughness at longitudinal turning using the different optimization methods. Technical Gazette, 17, 397–402.Google Scholar
  11. Kocyigit, N. (2015). Fault and sensor error diagnostic strategies for a vapour compression refrigeration system by using fuzzy inference systems and artificial neural network. International Journal of Refrigeration. doi: 10.1016/j.ijrefrig.2014.10.017.CrossRefGoogle Scholar
  12. Krizek, Z., Jurkovic, Z., & Brezocnik, M. (2008). Analytical study of different approaches to determine optimal cutting force. In 12th international research/expert conference on the trends in the development of machinery and associated technology (TMT). Istanbul, Turkey, August 26–30.Google Scholar
  13. Lela, B., Bajic, D., & Jozic, S. (2009). Regression analysis, support vector machines, and Bayesian neural network approaches to modelling surface roughness in face milling. The International Journal of Advanced Manufacturing Technology. doi: 10.1007/s00170-008-1678-z.CrossRefGoogle Scholar
  14. Mahesh, G., Muthu, S., & Devadasan, S. R. (2015). Prediction of surface roughness of end milling operation using genetic algorithm. The International Journal of Advanced Manufacturing Technology. doi: 10.1007/s00170-014-6425-z.CrossRefGoogle Scholar
  15. Mgwatu, M. I. (2013). Integrated approach for optimising machining parameters, tool wear and surface quality in multi pass turning operations. Advances in Production Engineering & Management. doi: 10.14743/apem2013.4.168.
  16. Saric, T., Simunovic, G., & Simunovic, K. (2013). Use of neural networks in prediction and simulation of steel surface roughness. International Journal of Simulation Modelling. doi: 10.2507/IJSIMM12(4)2.241.CrossRefGoogle Scholar
  17. Senthilkumar, N., Tamizharasan, T., & Anandakrishnan, V. (2013). An ANN approach for predicting the cutting inserts performances of different geometries in hard turning. Advances in Production Engineering & Management. doi: 10.14743/apem2013.4.170.
  18. Sivarao, Brevern, P., & El-Tayeb, N. S. M. (2009a). A new approach of adaptive network-based fuzzy inference system modeling in laser processing-A graphical user interface based. Journal of Computer Science. doi: 10.3844/jcssp.2009.704.710.CrossRefGoogle Scholar
  19. Sivarao, Brevern, P., El-Tayeb, N. S. M., & Vengkatesh, V. C. (2009b). Neural network multi-layer perceptron modeling for surface quality prediction in laser machining. Application in Machine Learning. doi: 10.5772/8612.Google Scholar
  20. Sivarao, Brevern, P., El-Tayeb, N. S. M., & Vengkatesh, V. C. (2009c). Modeling, testing and experimental validation of laser machining micro quality response by artificial neural network. International Journal of Engineering & Technology, 9(9), 161–166.Google Scholar
  21. Sivarao, Brevern, P., El-Tayeb, N. S. M., & Vengkatesh, V. C. (2009d). Modeling of laser processing cut quality by adaptive network-based fuzzy inference system (ANFIS). Journal of Mechanical Engineering Science. doi: 10.1243/09544062JMES1319.Google Scholar
  22. Smola, A. J., & Scholkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing. doi: 10.1023/B:STCO.0000035301.49549.88.CrossRefGoogle Scholar
  23. Tamang, S. K., & Chandrasekaran, M. (2015). Modeling and optimization of parameters for minimizing surface roughness and tool wear in turning Al/SiCp MMC, using conventional and soft computing techniques. Advances in Production Engineering & Management. doi: 10.14743/apem2015.2.192.CrossRefGoogle Scholar
  24. Tomar, D., & Agarwal, S. (2015). Twin support vector machine: A review from 2007 to 2014. Egyptian Informatics Journal. doi: 10.1016/j.eij.2014.12.003.CrossRefGoogle Scholar
  25. Vapnik, V. N. (1999). The nature of statistical learning theory. New York: Springer.Google Scholar
  26. Viharos, Z. J., & Kis, K. B. (2011). Support Vector Machine (SVM) based general model building algorithm for production control. In Preprints of the 18th International Federation of Automatic Control (IFAC) World Congress (pp. 14103–14108). Milano, Italy, August 28–September 2.Google Scholar
  27. Wang, H., Shao, H., Chen, M., & Hu, D. (2003). On-line tool breakage monitoring in turning. Journal of materials Processing Technology. doi: 10.1016/S0924-0136(03)00227-9.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Zoran Jurkovic
    • 1
    Email author
  • Goran Cukor
    • 1
  • Miran Brezocnik
    • 2
  • Tomislav Brajkovic
    • 1
  1. 1.Faculty of EngineeringUniversity of RijekaRijekaCroatia
  2. 2.Faculty of Mechanical EngineeringUniversity of MariborMariborSlovenia

Personalised recommendations