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
收藏
页码：17 / 28
页数：12
相关论文
共 50 条
  • [31] Comparison between artificial neural network and response surface methodology in the prediction of the production rate of polyacrylonitrile electrospun nanofibers
    Komeil Nasouri
    Ahmad Mousavi Shoushtari
    Mehrdad Khamforoush
    [J]. Fibers and Polymers, 2013, 14 : 1849 - 1856
  • [32] Comparison of Response Surface Methodology and Artificial Neural Network in Optimization and Prediction of Acid Activation of Bauxsol for Phosphorus Adsorption
    Ye, Jie
    Zhang, Panyue
    Hoffmann, Erhard
    Zeng, Guangming
    Tang, Yinan
    Dresely, Johanna
    Liu, Yang
    [J]. WATER AIR AND SOIL POLLUTION, 2014, 225 (12):
  • [33] Comparison between artificial neural network and response surface methodology in the prediction of the parameters of heat set polypropylene yarns
    Dadgar, Mehran
    Varkiyani, Seyed Mohammad Hosseini
    Merati, Ali Akbar
    [J]. JOURNAL OF THE TEXTILE INSTITUTE, 2015, 106 (04) : 417 - 430
  • [34] Optimization and prediction of incremental sheet forming parameters of Titanium grade 5 sheet using a response surface methodology and artificial neural network
    Ajay, Veera C.
    Elangovan, S.
    Kamaraja, A. S.
    Kumar, Karthik K.
    Kumar, Pratheesh S.
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 2023, 237 (08) : 1818 - 1833
  • [35] Analysis and prediction of the joint strength of friction stir welded Aluminium 5754 to polyamide using response surface methodology and artificial neural network
    Adarsh, S. J.
    Natarajan, Arivazhagan
    [J]. JOURNAL OF THERMOPLASTIC COMPOSITE MATERIALS, 2023, 36 (09) : 3644 - 3672
  • [36] Data on artificial neural network and response surface methodology analysis of biodiesel production
    Ayoola, A. A. .
    Hymore, F. K.
    Omonhinmin, C. A.
    Babalola, P. O.
    Bolujo, E. O.
    Adeyemi, G. A.
    Babalola, R.
    Olafadehan, O. A.
    [J]. DATA IN BRIEF, 2020, 31
  • [37] Comparison of response surface methodology with artificial neural network for prediction of the tensile properties of friction stir-processed surface composites
    Butola, Ravi
    Singari, Ranganath M.
    Murtaza, Qasim
    Tyagi, Lakshay
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING, 2022, 236 (01) : 126 - 137
  • [38] Response surface methodology to tune artificial neural network hyper-parameters
    Keser, Sinem Bozkurt
    Sahin, Yeliz Buruk
    [J]. EXPERT SYSTEMS, 2021, 38 (08)
  • [39] Artificial neural network modeling and response surface methodology of desalination by reverse osmosis
    Khayet, M.
    Cojocaru, C.
    Essalhi, M.
    [J]. JOURNAL OF MEMBRANE SCIENCE, 2011, 368 (1-2) : 202 - 214
  • [40] Novel approach to initial blank design in deep drawing using artificial neural network
    Haddadzadeh, M.
    Razfar, M. R.
    Mamaghani, M. R. M.
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE, 2009, 223 (10) : 1323 - 1330
12345