Prediction of Machining Forces using Neural Networks Trained by a Genetic Algorithm

This paper proposes the prediction of machining forces using multi-layered perceptron trained by genetic algorithm (GA). The data obtained from experimental results of a turning process are used to train the proposed artificial neural networks (ANNs) with three inputs to get machining forces as output. The optimal ANN weights are computed using GA search. This function-replacing hybrid made of GA and ANN is computationally efficient and accurate to predict the machining forces for the input machining conditions.