WRINKLING PREDICTION IN DEEP DRAWING BY USING RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORK

被引：4

作者：

Rafizadeh, Hossein ^{[1
]}

Azimifar, Farhad ^{[1
]}

Foode, Puya ^{[2
]}

Foudeh, Mohammad Reza ^{[3
]}

Keymanesh, Mohammad ^{[4
]}

机构：

[1] Islamic Azad Univ, Majlesi Branch, Dept Mech Engn, Esfahan, Iran

[2] Isfahan Univ Technol, Dept Mech Engn, Esfahan, Iran

[3] Daneshpajoohan Higher Educ Inst, Mech Engn Dept, Esfahan, Iran

[4] Golpayegan Univ Technol, Dept Mech Engn, PB 87717-65651, Golpayegan, Iran

来源：

TRANSACTIONS OF FAMENA | 2017年 / 41卷 / 02期

关键词：

ANN; deep drawing; FEM; RSM; wrinkling; METAL;

D O I：

10.21278/TOF.41202

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

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.

引用

页码：17 / 28

页数：12

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