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Here I introduce an automatic approach to determine the material flowpatterns during deformation process using artificial neural networks (ANN).Since deriving and calibrating complex mathematical models for prediction ofpower requirements in each individual deformation process is inconvenient, thegenerality of using streamline field method has been limited. I propose anautomatic approach to build and calibrate streamlines with ANN. The coordinatesof specific points within the deformation region were used as input and thestream function values on the points were used as output dataset in ANNtraining algorithm. A specific neural network architecture was then implementedto predict the flow patterns of the deforming body by an ANN-based streamlineequation. At the next step, the upper bound theorem was incorporated toestimate the force and power requirements for equal channel angular extrusion(ECAE) of a composite system under hot deformation. For verification of theresults, a finite element software was utilized in parallel to investigate theaccuracy of the proposed approach for estimation of force requirements. Thepredicted force requirements under various temperature ranges with ANN basedtechnique were consistent with that of the finite element predictions whichdemonstrates the accuracy of the proposed approach. The proposed approach maybe appropriate for fast simulation of a wide range of steady state materialforming processes.

Notes on derivation of streamline fields using artificial neural networks for automatic simulation of material forming processes