Application of finite element method and artificial neural network to the die design of radial forging processes

Abstract

This investigation adopts the finite element method (FEM) and the artificial neural network (ANN) to plan the radial forging of work-hardened materials to yield the optimal designed die. The process parameters considered herein are die corner radius (R), ring gap height (H), friction factor (m), workhardening coefficient (n), gap between the billet and die (c) and the punch load ( f). The accuracy of the FEM model constructed herein is established. Fifty sets of processing parameters are simulated by the FEM, and the results, together with the outer rims of the flange after forming, are taken as the learning file in ANN. Then, based on the range that is set by the learning file, another 20 sets of flange with different shapes than those in the test file are selected to obtain a combination of parameters of the die, materials and lubricants and other factors. During the design of the die, many tests are conducted, and flanges of similar shapes are found to be obtained with various combinations of processing parameters. This result indicate that the learning pattern presented herein meets the needs of all types of parameter combinations. Finally, based on the required specification of the shape of the outer rim of the flange, this work uses ANN to obtain all the specified processing parameters. Finiteelement analysis is then used to confirm the accuracy of the results and further investigate the effect of the related parameters on the flange shape. The following conclusion is drawn: The design of the die can yield finished flange products with similar shapes using different parameter combinations. During the forming process, a suitable range of parameters is selected for the die, the materials and the lubricant. Then, according to the strength of their effects, their inputs and output values are appropriately adjusted and the most suitable combination of processing parameters identified according to the similarities in the flange shapes they produce.

A Study on the Effects of Mass and Velocity Factor for the Dynamic-Explicit FEM in the Upsetting Process

“…In the past, modeling the manufacturing processes had been based on ideal condition with simple energy theory or empirical equations, it can be found in many excellent textbooks. The recent researches focus on the formability predictions [1], fracture predictions [2], and backward simulation with artificial neural network or fuzzy inference system [3,4].…”

Applications of Artificial Neural Network to Sheet Metal Work - A Review

“…Slomp and Klingenberg [33] proposed ANN for detection of punching/blanking characteristics from the Force-Displacement graph. Hsiang and Ho [34] investigated FEM and ANN methods to plan the radial forging of work-hardened materials to yield the optimal designed die. For the FEM analysis of radial forging process, they considered number of parameters like die corner radius, ring gap height, friction factor, work hardening coefficient, gap between the billet and die and the punch load etc.…”