Neural Network prediction of dry grinding by CBN grinding wheel using special conditioning Online publication date: Sat, 16-May-2009
by T. Tawakoli, M. Rabiey, M. Lewis
International Journal of Materials and Product Technology (IJMPT), Vol. 35, No. 1/2, 2009
Abstract: Grinding in general, as compared with other machining processes, offers advantages such as superior surface finish and low residual stresses. However, it is rather difficult to achieve these goals in particular with dry grinding due to the complicated nature of the process. Any change in grinding parameters or grinding wheel characteristics can have a great influence on process results. A specially-conditioned resin-bonded CBN grinding wheel was proposed earlier to reduce harmful heat generation in dry grinding and to optimise the process. A subsequent study was conducted using Neural Network method to further the advancement of this research. In this work a comparison of some of the very good results of Neural Network prediction is presented, which was carried out to characterise the complex interaction of parameters in dry grinding by special conditioning, based on the innovative concept. The results showed an excellent prediction of the grinding forces, roughness and workpiece surface burning by Neural Network analysis.
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