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Cold forged parts are mainly employed in automotive and aerospace assemblies, and strength plays an essential role in such applications. Backward extrusion is one such process in cold forging for the production of axisymmetrical cup-like parts, which is affected by a number of variables that influence the quality of the products. The study on the influencing parameters becomes necessary as the complexity of the part increases. The present paper focuses on the use of a multi-layered feed forward artificial neural network (ANN) model for determining the effects of process parameters such as billet size, reduction ratio, punch angle, and land height on forming behavior, namely, effective stress, strain, strain rate, and punch force in a cold forging backward extrusion process for AISI 1010 steel. Full factorial design (FFD) has been employed to plan the finite element (FE) simulations and accordingly, the input variables and response patterns are obtained for training from these FE simulations. This ANN model-based analysis reveals that the forming behavior of the cold forging backward extrusion process tends to increase with the billet size as well as the reduction ratios. However, decreases in punch angle and land height lead to the reduction of punch forces, which in turn enhances the punch life. FE simulation along with the developed ANN model scheme would benefit the cold forging industry in minimizing the process development effort in terms of cost and time. |
