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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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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