On-line Surface Roughness Prediction in Grinding Using Recurrent Neural Networks
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.
KeywordsGrinding Surface roughness Dynamic modelling Recurrent neural networks
Unable to display preview. Download preview PDF.
- 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
- 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
- 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.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
- 15.Beale, M.H., Hagan, M.T., Demuth, H.B.: Neural Network ToolboxTM User’s Guide. The MathWorks Inc., Natick (2012)Google Scholar