Application of artificial neural network and genetic algorithm to predict and optimize load and torque in T-section profile ring rolling

Abstract

Artificial neural network is implemented to predict the required load and torque in T-section profile ring rolling process for the first time in this study. Moreover, an optimal condition of T-section profile ring rolling process for specific limit of input factor is acquired using genetic algorithm technique. Various three-dimensional finite element simulations are carried out for different collections of process variables to obtain initial data for training and validation of the neural network. Besides, the finite element model is verified via comparison with the experimental results of the other investigators. The back-propagation algorithm is utilized to develop Levenberg–Marquardt feed-forward network and the optimum architecture is achieved by estimating the performance considering different number of hidden layers and neurons. It is concluded that results of artificial neural network predictions have an appropriate conformity with those ones from simulation and experiments. Moreover, a reasonable accuracy is obtained from the implemented model by which the prediction of ring rolling load and torque in different conditions can be achieved.

Design optimization of a shell and tube heat exchanger with staggered baffles using neural network and genetic algorithm

“…Parvizi A et al. 22 implemented ANN for predicting the required torque and load in the T-section profile ring rolling process and an optimal condition of the process for specific input factor limit is acquired using a genetic algorithm. Data for training and validation of neural network is obtained from 3 D simulations.…”