Improvement in Material Flow During Nonisothermal Warm Deep Drawing of Nonheat Treatable Aluminum Alloy Sheets

2016 ◽  
Vol 139 (3) ◽  
Author(s):  
Sudhy S. Panicker ◽  
Sushanta Kumar Panda
Keyword(s):  
Aluminum Alloy ◽  
Temperature Gradient ◽  
Deep Drawing ◽  
Material Flow ◽  
Alloy Sheet ◽  
Effect Of Temperature ◽  
Fe Model ◽  
Drawing Test ◽  
Warm Deep Drawing ◽  
Deep Drawing Test

Automotive industries are very much interested in implementing warm forming technology for fabrication of light weight auto-body panels using aluminum alloys without localized thinning or splitting. A nonheat treatable and low formable AA5754-H22 aluminum alloy sheet was selected in the present work, and a laboratory scale warm deep drawing test set-up and process sequences were designed to improve material flow through independent heating of punch and dies. Significant enhancement in cup depth was observed when the temperature of punch and dies were set to 30 °C and 200 °C, respectively. Thermo-mechanical finite-element (FE) model of the nonisothermal deep drawing test was developed successfully to study the improvement in material flow incorporating Barlat-89 yield theory using temperature dependent anisotropy coefficients and Cowper–Symonds hardening model. It was found that a nonisothermal temperature gradient of approximately 93 °C was established within the blank from the center to flange at the start of deformation, and subsequent evolution of temperature gradient helped in improving material flow into the die cavity. The effect of temperature gradient on forming behavior in terms of cup height, ear profile, and thinning development across flange, cup wall, and blank center were predicted and validated with experimental results.

Improvement in Material Flow During Non-Isothermal Warm Deep Drawing of Non-Heat Treatable Aluminum Alloy Sheets

2016 ◽  
Author(s):  
Sudhy S. Panicker ◽  
Sushanta Kumar Panda
Keyword(s):  
Aluminum Alloy ◽  
Temperature Gradient ◽  
Deep Drawing ◽  
Material Flow ◽  
Element Model ◽  
Alloy Sheet ◽  
Effect Of Temperature ◽  
Drawing Test ◽  
Warm Deep Drawing ◽  
Deep Drawing Test

Automotive industries are very much interested in implementing warm forming technology for fabrication of light weight auto-body panels using aluminum alloys without localized thinning or splitting. A non-heat treatable and low formable AA5754-H22 aluminum alloy sheet was selected in the present work, and a laboratory scale warm deep drawing test set-up and process sequences were designed to improve material flow through independent heating of punch and dies. Significant enhancement in cup depth was observed when the temperature of punch and dies were set to 30°C and 200°C respectively. Thermo-mechanical finite element model of the non-isothermal deep drawing test was developed successfully to study the improvement in material flow incorporating Barlat-89 yield theory using temperature dependent anisotropy coefficients and Cowper-Symonds hardening model of AA5754-H22 material. It was found that a non-isothermal temperature gradient of approximately 93°C was established within the blank from the center to flange at the start of deformation, and subsequent evolution of temperature gradient helped in improving material flow into the die cavity. The effect of temperature gradient on forming behavior in terms of cup height, ear profile and thinning development across flange, cup wall, and blank center were predicted and validated with experimental results.

Cazacu and Barlat Criterion Identification Using the Cylindrical Cup Deep Drawing Test and the Coupled Artificial Neural Networks – Genetic Algorithm Method

2012 ◽  
Vol 504-506 ◽  
pp. 637-642 ◽  
Author(s):  
Hamdi Aguir ◽  
J.L. Alves ◽  
M.C. Oliveira ◽  
L.F. Menezes ◽  
Hedi BelHadjSalah
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Deep Drawing ◽  
Inverse Analysis ◽  
Computational Time ◽  
Ann Model ◽  
Drawing Test ◽  
Artificial Neural ◽  
Deep Drawing Test ◽  
Cylindrical Cup

This paper deals with the identification of the anisotropic parameters using an inverse strategy. In the classical inverse methods, the inverse analysis is generally coupled with a finite element code, which leads to a long computational time. In this work an inverse analysis strategy coupled with an artificial neural network (ANN) model is proposed. This method has the advantage of being faster than the classical one. To test and validate the proposed approach an experimental cylindrical cup deep drawing test is used in order to identify the orthotropic material behaviour. The ANN model is trained by finite element simulations of this experimental test. To reduce the gap between the experimental responses and the numerical ones, the proposed method is coupled with an optimization procedure based on the genetic algorithm (GA) to identify the Cazacu and Barlat’2001 material parameters of a standard mild steel DC06.

Evaluation of stamping lubricants using the deep drawing test

2007 ◽  
Vol 47 (14) ◽  
pp. 2120-2132 ◽  
Author(s):  
Hyunok Kim ◽  
Ji Hyun Sung ◽  
Rajesh Sivakumar ◽  
Taylan Altan
Keyword(s):  
Deep Drawing ◽  
Drawing Test ◽  
Deep Drawing Test

Thermal Deep Drawing of Magnesium Alloy Sheets

2014 ◽  
Vol 971-973 ◽  
pp. 220-223
Author(s):  
Bo Wang ◽  
Xian Chang Mao
Keyword(s):  
Magnesium Alloy ◽  
Deep Drawing ◽  
Development Trend ◽  
Alloy Sheet ◽  
Solid Particles ◽  
Blank Holder ◽  
Forming Technology ◽  
Warm Deep Drawing ◽  
Plastic Forming ◽  
The Development Trend

Thermal deep-drawing is a main processing technology which is used to plastic forming for magnesium alloy sheet. Combined with the research status, several deep drawing methods were introduced in detail in this paper, including indirect heating deep drawing, direct heating deep drawing, differential temperature deep drawing, warm hydraulic deep drawing, warm deep drawing with solid particles, warm deep drawing with segmental blank-holder, pre-forming warm deep drawing. Finally, the development trend and burning questions of magnesium alloy thermal forming technology were predicted.

Numerical Simulation of Bulging Process of Aluminum Alloy Sheet

2013 ◽  
Vol 327 ◽  
pp. 112-116 ◽  
Author(s):  
Mao Ting Li ◽  
Yong Zhang ◽  
Chui You Kong
Keyword(s):  
Aluminum Alloy ◽  
Material Flow ◽  
Thickness Distribution ◽  
Superplastic Forming ◽  
Alloy Sheet ◽  
Pressure Loading ◽  
Forming Process ◽  
Element Analysis ◽  
Danger Zone ◽  
Linear Finite Element

Basing on software MSC. Marc of non-linear finite element analysis, the article has studied the material flow in the process of aluminum alloy superplastic gas bulging forming. By analyzing of the thickness distribution of the molding member it confirm the danger zone in the forming process. By analyzing of pressure loading curve influence on forming part. Because the aluminum alloy is widely used in the industrial departments, it is supposed to improve the ability of forming ability of aluminum alloy by researching the superplastic forming.

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