Construction of a prediction model for the structural stability of a surface grinder using backpropagation neural network

Learning and prediction capability of the backpropagation neural network (BPNN) have been used to build the prediction model for the structural stability of a surface grinder. The Lagrange energy method is applied to derive the dynamic equation of the lumped parameter model of the surface grinder. The major factors influencing the structural stability of the system can be determined after the ratio of kinetic energy of the sub-structure and the ratio of potential energy of the sub-structure interface are obtained. An orthogonal rotatable central composite design is adopted to dispose the treatment combinations of the major factors. The BPNN model is constructed by the treatment combinations of the training patterns and verified by the treatment combinations of the test patterns. In this paper, a 3-layer BPNN model with a 10-neuron hidden layer which converged after 4,072 learning cycles is selected to predict the structural stability of a surface grinder within the planned ranges. The percentage residuals of both training patterns and test patterns are all within 3.41%, thus the prediction accuracy of the BPNN model is excellent so that the engineering demands are well satisfied.