A new approach for dynamic modelling of energy consumption in the grinding process using recurrent neural networks. Issue 6 (August 2016)

• Record Type: Journal Article
• Authors: Arriandiaga, A.; Portillo, E.; Sánchez, J.; Cabanes, I.; Pombo, I.
• Is Part Of: Neural computing & applications. Volume 27:Issue 6(2016)
• Page Start: 1577
• Page End: 1592
• Publication Date: 2016-08
• Subjects: Specific grinding energy -- Dynamic modelling -- Complete time series -- Recurrent neural networks -- Static inputs -- Generalization; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1957-1
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract Grinding is a critical machining process because it produces parts of high precision and high surface quality. Due to the semi-artisan production of the wheel, it is not possible to know in advance the performance of the wheel. One of the most useful parameters to characterize the grinding process is the specific grinding energy, which varies with the wear of the grinding wheel during its lifecycle. Thus, it would be useful to model the specific grinding energy in order to get information about the performance of the wheel before buying it. Unlike the typical applications of time series forecasting, in this work, a totally different issue is presented: the prediction of new and complete time series bounded in time without initial or historic values. In this context, an analysis of the effect of the time characteristics and the number of points of the time series on the prediction capabilities of the ANN is presented. The results of the analysis show that 200 points are enough to predict a complete time series up to 2000 mm3 /mm of specific volume of material removed. Actually, it is shown that modelling the evolution of the grinding specific energy up to 2000 mm3 /mm is possible. The net shows good capability to generalize to new grinding conditions, with errors below 23.65 %, and to new wheel characteristics, with errors below 20.01 %, which are satisfactory from the grinding process perspective.