Improved method for grinding force prediction based on neural network

The work presented in this paper is an investigation of the prediction of amplitudes of the specific grinding force components. An improved method for artificial neural networks (ANNs) establishment is proposed here allowing accurate prediction of specific normal and tangential grinding forces. This method can determine the optimal set of inputs to be used for these ANN. This set of inputs is composed of significant factors and interactions among factors that could possibility offer the best learning and generalization of ANNs simultaneously. A 48-run experimental design (MED) is used in this research to train the ANNs and a total of 81 experiments are conducted to test the generalization performances of ANNs. Results have indicated that the developed ANNs show low deviations from the training data, and acceptable deviations from the testing data. In addition, the accuracies of these ANNs are found to be significantly better than those of other approaches used for modelling of the specific grinding force components. These approaches use regression models, power models, genetic algorithms or the common ANNs for which only factors of the MED are usually used in the input layer.