Optimization of Open Die Ironing Process through Artificial Neural Network for Rapid Process Simulation

Abstract

The open die forging sequence design and optimization are usually performed by simulating many different configurations corresponding to different forging strategies. Finite element analysis (FEM) is a tool able to simulate the open die forging process. However, FEM is relatively slow and therefore it is not suitable for the rapid design of online forging processes. A new approach is proposed in this work in order to describe the plastic strain at the core of the piece. FEM takes into account the plastic deformation at the core of the forged pieces. At the first stage, a thermomechanical FEM model was implemented in the MSC.Marc commercial code in order to simulate the open die forging process. Starting from the results obtained through FEM simulations, a set of equations describing the plastic strain at the core of the piece have been identified depending on forging parameters (such as length of the contact surface between tools and ingot, tool’s connection radius, and reduction of the piece height after the forging pass). An Artificial Neural Network (ANN) was trained and tested in order to correlate the equation coefficients with the forging to obtain the behavior of plastic strain at the core of the piece.