On-line Surface Roughness Prediction in Grinding Using Recurrent Neural Networks

  • Ander ArriandiagaEmail author
  • Eva Portillo
  • Jose A. Sánchez
  • Itziar Cabanes
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 517)


Grinding is a key process in high-added value sectors due to its capacity for producing high surface quality and high precision parts. One of the most important parameters that indicate the grinding quality is the surface roughness (R a ). Analytical models developed to predict surface finish are not easy to apply in the industry. Therefore, many researchers have made use of Artificial Neural Networks. However, all the approaches provide a particular solution for a wheel-workpiece pair. Besides, these solutions do not give surface roughness values related to the grinding wheel status. Therefore, in this work the prediction of the surface roughness (R a ) evolution based on Recurrent Neural Networks is presented with the capability to generalize to new grinding wheels and conditions. Results show excellent prediction of the surface finish evolution. The absolute maximum error is below 0.49µm, being the average error around 0.32µm.


Grinding Surface roughness Dynamic modelling Recurrent neural networks 


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  1. 1.
    Marinescu, I.D., Hitchiner, M.P., Uhlmann, E., Rowe, W.B., Inasaki, I.: Handbook of Machining with Grinding Wheels. CRC Press, Boca Raton (2006)CrossRefGoogle Scholar
  2. 2.
    Jiang, J., Ge, P., Hong, J.: Study on micro-interacting mechanism modeling in grinding process and ground surface roughness prediction. Int J Adv Technol 67(5–8), 1035–1052 (2008)Google Scholar
  3. 3.
    Agarwal, S., Rao, P.V.: Modeling and prediction of surface roughness in ceramic grinding. Int. J. Mach. Tools Manuf. 50(12), 1065–1076 (2010)CrossRefGoogle Scholar
  4. 4.
    Aguiar, P.R., Cruz, C.E.D., Paula, W.C.F.: Predicting surface roughness in grinding using neural networks. In: Advances in Robotics, Automation and Control, Vienna, pp. 33–44 (2008)Google Scholar
  5. 5.
    Vafaeesefat, A.: Optimum creep feed grinding process conditions for Rene 80 supper alloy using neural network. Int. J. Precis. Eng. Manuf. 10, 5–11 (2009)CrossRefGoogle Scholar
  6. 6.
    Sedighi, M., Afshari, D.: Creep feed grinding optimization by an integrated GA-NN system. J. Intell. Manuf. 21, 657–663 (2010)CrossRefGoogle Scholar
  7. 7.
    Yang, Q., Jin, J.: Study on machining prediction in plane grinding based on artificial neural network. In: Proceedings of International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Hangzhou, China, November 15–16, 2010Google Scholar
  8. 8.
    Li, G., Liu, J.: On-line prediction of surface roughness in cylindrical traverse grinding based on BP+GA algorithm. In: Proceedings of Second International Conference on Mechanic Automation and Control Engineering (MACE), Hohhot, China, pp. 1456–1459, July 15–17, 2011Google Scholar
  9. 9.
    Nandi, A.K., Pratihar, D.K.: Design of a genetic-fuzzy system to predict surface finish and power requirement in grinding. Fuzzy Sets Syst. 148, 487–504 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Ticknor, J.L.: A Bayesian regularized artificial neural network for stock market forecasting. Experts Systems with Applications 40(14), 5501–5506 (2013)CrossRefGoogle Scholar
  11. 11.
    Claveria, O., Torra, S.: Forecasting tourism demand in Catalonia: Neural networks vs. time series models. Economic Modelling 36, 220–228 (2013)CrossRefGoogle Scholar
  12. 12.
    Wu, C.L., Chau, K.W.: Prediction of rainfall time series using modular soft computing methods. Engineering Applications of Artificial Intelligence 26(3), 997–1007 (2013)CrossRefGoogle Scholar
  13. 13.
    Godarzi, A.A., Amiri, R.M., Talaei, A., Jamasb, T.: Predicting oil price movements: A dynamic Artificial Neural Network approach. Energy Policy 68, 371–382 (2014)CrossRefGoogle Scholar
  14. 14.
    Pisoni, E., Farina, M., Carnevale, C., Piroddi, L.: Forecasting peak air pollution levels using NARX models. Engineering Applications of Artificial Intelligence 22(4–5), 593–602 (2009)CrossRefGoogle Scholar
  15. 15.
    Beale, M.H., Hagan, M.T., Demuth, H.B.: Neural Network ToolboxTM User’s Guide. The MathWorks Inc., Natick (2012)Google Scholar
  16. 16.
    Arriandiaga, A., Portillo, E., Sánchez, J.A., Cabanes, I., Pombo, I.: Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process. Sensors 14, 8756–8778 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ander Arriandiaga
    • 1
    Email author
  • Eva Portillo
    • 1
  • Jose A. Sánchez
    • 2
  • Itziar Cabanes
    • 1
  1. 1.Department of Automatic Control and System EngineeringUniversity of the Basque CountryBilbaoSpain
  2. 2.Department of Mechanical EngineeringUniversity of the Basque CountryBilbaoSpain

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