Neural network-based expert system for modeling of tube spinning process

Abstract

The present paper deals with the development of neural network (NN)-based expert system for modeling of 2024 aluminum tube spinning process. Tube spinning is a highly nonlinear thermo-mechanical process for producing large-diameter thin-walled shapes. It is interesting to note that the performance of the process depends on various process parameters, such as wall thickness, percentage of thickness reduction, feed rate, mandrel rotational speed, solution treatment time and aging time. Therefore, an NN-based expert system is necessary for modeling the tube spinning process. The input layer of NN consists of six neurons corresponding to the inputs of the tube spinning process. Moreover, the output layer consists of four neurons that represent four responses, namely change in diameter, change in thickness, inner and outer surface roughness. It is to be noted that the performance of NN depends on various factors, such as number of neurons in the hidden layer, coefficients of transfer functions and connecting weights, etc. In the present paper, three algorithms, such as back-propagation, genetic and artificial bee colony algorithms, are used for optimizing the said variables of NN. Further, the developed approaches are tested for their accuracy in prediction with the help of some test cases and found to model the tube spinning process effectively.