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

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.

Roll Design Method of Single–Stage Profile Ring Rolling Based on Modified UVDET

The aim of this study is to suggest the die design method for single-profile ring rolling in the production of a large scale welding neck flange, based on the modified uniform volume distribution element technique (UVDET). UVDET is presented as the ratio of volume change of ring blanks to that of undeformed rings per unit height. A welding neck flange has only one–side contour without a symmetry plane and the highly different volume distribution. To manufacture this type of ring, the blank preforming is recommended before a single–stage profile ring rolling process. FE simulations were performed to test the validity of proposed die design method, under Forge software environment.