prediction and optimization of load and torque in ring rolling process through development of artificial neural network and evolutionary algorithms

Developing artificial neural network (ann), a model to make a correct prediction of required force and torque in ring rolling process is developed for the first time. moreover, an optimal state of process for specific range of input parameters is obtained using genetic algorithm (ga) and particle swarm optimization (pso) methods. radii of main roll and mandrel, rotational speed of main roll, pressing velocity of mandrel and blank size are considered as input parameters. furthermore, the required load and torque in ring rolling process are taken into account as process outputs. various three dimensional finite element simulations are performed for different sets of process variables to achieve preliminary data for training and validation of the neural network. besides, the finite element model is approved via comparison with the experimental results of the other investigators. the back propagation (bp) algorithm is considered to develop levenberg–marquardt feed-forward network. additionally, model responses analysis is carried out to improve the understanding of the behavior of the ann model. it is concluded that results of ann predictions have an appropriate conformity with those from simulation and experiments. moreover, ga and pso methods have been implemented to obtain the optimal state of process while their outcomes have been also compared.