FEM and ANN Analysis in Fine-Blanking Process

Abstract

Fine-blanking (FB) is an effective and economical shearing process that offers a precise and clean cutting-edge finish, eliminates unnecessary secondary operations, and increases quality. In the traditional blanking product development paradigm, the design of the formed product and tooling is usually based on know-how and experience, which are generally obtained through long years of apprenticeship and skilled craftsmanship. In this study, the possibility of using finite element method (FEM) together with artificial neural networks (ANN) was investigated to analysis the fine-blanking process. Finite element analysis was used to simulate the process with an isotropic elastic–plastic material model. The results compare well with experimental results available in the literature; after confirming the validity of the model with experimental data, a number of parameters such as V-ring height effect, punch and holder force on die-roll, hydrostatic pressure status as an important factor in increasing burnish zone, and accuracy of part and radial stress status as a factor in increasing die erosion, which were also used for training the ANN, were considered. Finally, numerical data were used to train neural networks. The Levenberg–Marquardt (LM) algorithm with three neurons in the hidden layer (LM-3) appeared to be the most optimal topology and gives the best results. It was found that the coefficient of multiple determinations (R 2 value) between the FEM and ANN predicted data is equal to about 0.999 for the size of die-roll, therefore indicating the possibility of FEM and ANN as a powerful design tool for the fine-blanking process.