Neural network controller for turning operation of a metal cutting process

In this paper, the design and implementation of an effective neural network(NN) for process identification as well as a neural network controller to track a desired cutting force under payload variations are presented. The proposed learning algorithm for the NN is backpropagation. In addition, an online controller algorithm is developed for the turning operation to track the desired force trajectory as closely as possible in spite of wide ranges of disturbances and payload variations for each metal cutting task. The simulation experiments demonstrate the effectiveness of the proposed method