Abstract
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.
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References
Marinescu, I.D., Hitchiner, M.P., Uhlmann, E., Rowe, W.B., Inasaki, I.: Handbook of Machining with Grinding Wheels. CRC Press, Boca Raton (2006)
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)
Agarwal, S., Rao, P.V.: Modeling and prediction of surface roughness in ceramic grinding. Int. J. Mach. Tools Manuf. 50(12), 1065–1076 (2010)
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)
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)
Sedighi, M., Afshari, D.: Creep feed grinding optimization by an integrated GA-NN system. J. Intell. Manuf. 21, 657–663 (2010)
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, 2010
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, 2011
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)
Ticknor, J.L.: A Bayesian regularized artificial neural network for stock market forecasting. Experts Systems with Applications 40(14), 5501–5506 (2013)
Claveria, O., Torra, S.: Forecasting tourism demand in Catalonia: Neural networks vs. time series models. Economic Modelling 36, 220–228 (2013)
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)
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)
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)
Beale, M.H., Hagan, M.T., Demuth, H.B.: Neural Network ToolboxTM User’s Guide. The MathWorks Inc., Natick (2012)
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)
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Arriandiaga, A., Portillo, E., Sánchez, J.A., Cabanes, I. (2015). On-line Surface Roughness Prediction in Grinding Using Recurrent Neural Networks. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2015. Communications in Computer and Information Science, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-319-23983-5_3
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DOI: https://doi.org/10.1007/978-3-319-23983-5_3
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