An integrated FEM and ANN methodology for metal-formed product design

Research output: Journal article publicationJournal articleAcademic researchpeer-review

28 Citations (Scopus)

Abstract

In the traditional metal-formed product development paradigm, the design of metal-formed product and tooling is usually based on heuristic know-how and experiences, which are generally obtained through long years of apprenticeship and skilled craftsmanship. The uncertainties in product and tooling design often lead to late design changes. The emergence of finite element method (FEM) provides a solution to verify the designs before they are physically implemented. Since the design of product and tooling is affected by many factors and there are many design variables to be considered, the combination of those variables comes out with various design alternatives. It is thus not pragmatic to simulate all the designs to find out the best solution as the coupled simulation of non-linear plastic flow of billet material and tooling deformation is very time-consuming. This research is aimed to develop an integrated methodology based on FEM simulation and artificial neural network (ANN) to approximate the functions of design parameters and evaluate the performance of designs in such a way that the optimal design can be identified. To realize this objective, an integrated FEM and ANN methodology is developed. In this methodology, the FEM simulation is first used to create training cases for the ANN(s), and the well-trained ANN(s) is used to predict the performance of the design. In addition, the methodology framework and implementation procedure are presented. To validate the developed technique, a case study is employed. The results show that the developed methodology performs well in estimation and evaluation of the design.
Original languageEnglish
Pages (from-to)1170-1181
Number of pages12
JournalEngineering Applications of Artificial Intelligence
Volume21
Issue number8
DOIs
Publication statusPublished - 1 Dec 2008

Keywords

  • Artificial neural network
  • Design solution evaluation
  • Die design
  • FEM simulation
  • Metal forming
  • Metal-formed product design

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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