Grinding Forces Prediction Based Upon Experimental Design and Neural Network Models

The results presented are related to the prediction of the specific grinding force components. The main problems associated with the prediction capability of empirical models developed using the design of experiment (DOE) method are given. In this study an approach suggesting the combination of DOE method and artificial neural network (ANN) is developed. The inputs of the developed ANNs were selected among the factors and interaction between factors of the DOE depending on their significance at different confidence levels expressed by the value of α%. Results have shown particularly, the existence of a critical input set which improves the learning ability of the constructed ANNs. The built ANNs using these critical sets have shown low deviation from the training data and an acceptable deviation from the testing data. A high prediction accuracy of these ANNs was tested between models constructed using the developed approach and models developed by previous investigations.