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Go to Editorial ManagerThe dieless drawing process is an innovative method emanated and appeared in coincidence with development of the concept of metal superplasticity. It is utilized from the local heating of a wire or tube to a specified temperature and followed by a local cooling, so an additional deformation is inhibited. In this study, a special dieless drawing machine was designed to carry out an experimental program on SUS304-stainless steel wire having diameter of (1.6-2) mm to investigate the main process parameters such as speeds, heat quantity, heating coil width and heating-cooling separation distance. Also, a numerical model based on thermo-mechanical analysis was developed and validated with experimental program. Furthermore, an artificial neural network ANN model based on current experimental data was prepared to predict the dieless drawing behavior. A maximum area reduction of 40.7% was obtained in single pass. A 3.12mm/s feeding velocity and 4.97mm/s drawing velocity were realized through the experimental tests. The results showed that both drawing force and wire profile were effected by increasing of feeding speed, heating coil width and separation distance. Also, it is confirmed that strain rate was reduced by increasing the heating coil width and the reduction ratio was promoted. A maximum error of 21% was recorded between ANN model and experimental results. The results showed a good agreement among experimental, numerical and ANN models.
An experimental study has been implemented to study the effect of the central radial groove on the bearing pressure distribution. This study is based on the artificial neural network in the prediction of the complex and uncertain positions. Both width and depth of the groove have been varied at some magnitudes in order to investigate their effects on the pressure distribution and the stability of the bearing. Also, the effect of the groove parameters on the noise at the bearing situation in the systems have been analyzed and discussed. The results show that the use of neural network in the prediction of some points with range is very powerful in the minimization of the overall cost of groove design.
Impact energy prediction of thermal aged cast stainless steel from impact test was studied using artificial neural network (ANN) modeling. Impact energy data for specimens from eleven cast stainless steel alloys at different aging times and temperatures, were used to evaluate possible artificial neural network architecture for prediction impact energy. These data are taken from Argonne National Laboratories (ANL) in USA that involved impact test results of cast stainless steel after aging between 200 and 400oC for up to 30000 hour. The ANN model exhibited excellent comparison with experimental results of ANL i.e. correlation coefficient (R=0.9451) and mean square error (MSE=1.2*10-5). Since a large number of variables were used during training the ANN model, a reliable and useful predictor for impact energy in thermal aged cast stainless steel was provided.