This paper deals with the extrusion of gear-like profiles and uses of finite element method (FEM) and artificial neural network (ANN) to predict the extrusion load. In the study, gear-like components has been manufactured by forward extrusion for the AA1070 aluminum alloy and the process was simulated by using a DEFORM-3D software package to establish a database in order to provide the data for ANN modeling. Serious experiments were performed for only one die set and four teeth gear profile to obtain data for comparing with DEFORM-3D results. After verifying a highly appropriate FEM simulation with the experiment at the same conditions, Results were enhanced for different die lengths, extrusion ratios, and two extra teeth number as three and six using FEM simulations. Subsequently, the data from the performed FEM simulations were submitted for the best obtained ANN model. Finally, a good agreement between FEsimulated and ANN-predicted results was obtained. The proposed ANN model is found to be useful in predicting the forming load of the different die set variations based on the reliable test data.

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The main purpose of this research is to investigate the minimum deformation load by selecting a suitable forming method for manufacturing of gear-like sections and to compare the load estimation methods between Upper Bound Analysis and DEFORM-3D. Forward and lateral extrusion were chosen as two different forming methods. The effect of die transition geometry on deformation load was also investigated by straight tapered and cosine profiles. A newly kinematical admissible velocity field to analyze different profiles of extrusion dies was proposed by upper bound analysis. Al 1070 was used as working material. Experiments using five sets of dies with gear-like form were performed, and the measured forming load results were compared with the predictions of the theoretical solutions. Experiments were carried out on the 150 metric ton hydraulic press.