A neural network approach for grinding processes: Modelling and optimization

Abstract

The objective of this study is to show how back propagation (BP) neural networks can be used to model and optimize grinding processes, using creep feed grinding of alumina with diamond wheels as an example. First, a generalized back propagation neural network with two-hidden layers is used to establish the process model. Then the back propagation algorithm with Boltzmann factor is used to find the global optimal settings for the grinding process. From the simulation results obtained, it is found that the implemented neural network approach yields a more accurate process model than the regression method. It is also shown that, unlike the conventional back propagation network, proper use of the Boltzmann factor with BP can effectively avoid local minima and generate the global optimal solution.