An optimal estimation for neural network by using genetic algorithm for the prediction of thermal deformation in machine tools

Thermal deformations cause 40-70% error during the manufacturing process for the machine tools. In order to improve the accuracy of the machine tools, this study proposes a hybrid model, which predicts thermal deformation by combining an ARIMA and a feed-forward neural network (FNN) models. The genetic algorithm (GA) method is used to optimize this prediction model. The GA is used to search the optimal normalization coefficients, number of ARMA outputs and number of hidden neurons of FNN. It can reduce the network size and improve the propagation accuracy. In this study, comparisons between conventional FNN and the proposed hybrid model with or without using GA. The compared results show that the proposed hybrid model has better accuracy than the conventional FNN model and most accurate can be obtained by the proposed hybrid using GA. The predicted results, the hybrid model with GA can reduce the thermal deformation to 2 μm.