Original scientific paper

https://doi.org/10.21278/TOF.41202

Wrinkling Prediction in Deep Drawing by Using Response Surface Methodology and Artificial Neural Network

Hossein Rafizadeh orcid.org/0000-0002-3443-4494 ; Department of Mechanical Engineering, Majlesi Branch, Islamic Azad University, Isfahan, Iran
Farhad Azimifar ; Department of Mechanical Engineering, Majlesi Branch, Islamic Azad University, Isfahan, Iran
Puya Foode ; Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran
Mohammad Reza Foudeh ; Daneshpajoohan Higher Education Institute, Mechanical Engineering Department, Esfahan, Iran
Mohammad Keymanesh ; Department of Mechanical Engineering, Golpayegan University of Technology, Golpayegan, Iran

Abstract

The objective of this study is to predict influences of tooling parameters such as die and punch radius, blank holder force and friction coefficient between the die and the blank surfaces in a deep drawing process on the wrinkling height in aluminium AA5754 by using the response surface methodology (RSM) and an artificial neural network (ANN). The 3D finite element method (FEM), i.e. the Abaqus software, is employed to model the deep drawing process. In order to investigate the accuracy of this model, the results are compared with experimental results. The data derived from the FEM are used for modelling the RSM and training an ANN. Finally, the RSM and ANN outputs are compared so as to select the best model. The results of the two methods are promising and it is found that the ANN results are more accurate than the RSM results.

Keywords

ANN; deep drawing; FEM; RSM; wrinkling

Hrčak ID:

183698

URI

https://hrcak.srce.hr/183698

Publication date:

24.7.2017.

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