Experimental study and artificial neural networks prediction of effective parameters in continuous dieless wire drawing

Abstract EN

The 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.