A Deep Drawing Test for Determining the Punch Coefficient of Friction

1981 ◽  
Vol 103 (2) ◽  
pp. 197-202 ◽  
Author(s):  
S. Rajagopal
Keyword(s):  
Experimental Data ◽  
Coefficient Of Friction ◽  
Deep Drawing ◽  
Stress System ◽  
Drawing Test ◽  
The Coefficient Of Friction ◽  
Cylindrical Region ◽  
Deep Drawing Test ◽  
Lubrication Conditions

A deep drawing test is described for measuring the coefficient of friction between the cylindrical surfaces of the punch and the cup wall. The test is based on an analysis of the stress system in the cylindrical region of contact. Experimental data are presented for steel and aluminum blanks deep-drawn into cups under different lubrication conditions, and the advantages and limitations of the method are discussed.

Measurement of Coefficient of Friction in Hot Stamping by Hot Deep Drawing Test

2016 ◽  
Vol 716 ◽  
pp. 184-189
Author(s):  
Hironori Sasaki ◽  
Tomonori Mukai ◽  
Akira Yanagida
Keyword(s):  
Coefficient Of Friction ◽  
Deep Drawing ◽  
Hot Stamping ◽  
High Strength ◽  
Local Deformation ◽  
Wall Area ◽  
Scale Thickness ◽  
Drawing Test ◽  
The Coefficient Of Friction ◽  
Deep Drawing Test

Hot stamping process has been developed to produce the steel automobile parts with an ultra-high-strength of 1500 MPa. The effect of scale thickness on the formability in hot stamping was investigated by a hot deep drawing test in our previous research. The draw-in lengths of flange increased with decreasing the scale thickness. It is supposed that thin scale thickness resulted in low coefficient of friction at the flange area. The other reason is the temperature of wall zone would become low according to decreasing the scale thickness or increasing of the thermal transfer coefficient and it slightly inhibits local deformation at the wall area. It is difficult to separate these phenomena. To quantify the effect of scale thickness on the friction at the flange area during hot deep drawing, the coefficient of friction was directly measured. The coefficient of friction decreases with decreasing scale thickness.

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

Evaluation of drawing force by a new dimensionless method in deep drawing process

2020 ◽  
Vol 234 (13) ◽  
pp. 1604-1614
Author(s):  
Saeed Hajiahmadi ◽  
Majid Elyasi ◽  
Mohsen Shakeri
Keyword(s):  
Coefficient Of Friction ◽  
Deep Drawing ◽  
Experimental Tests ◽  
Geometric Parameters ◽  
Relational Model ◽  
Geometrical Parameters ◽  
Drawing Force ◽  
Dimensionless Parameters ◽  
Dimensionless Analysis ◽  
The Coefficient Of Friction

In this research, geometric parameters were given in dimensionless form by the Π- Buckingham dimensional analysis method in the dimensionless group for deep drawing of a round cup. To find the best group of dimensionless parameters and the fittest dimensionless relational model, three scales of the cup are evaluated numerically by a commercial finite element software and stepwise regression modeling. After analyzing all effective geometric parameters, a fittest relational model among dimensionless parameters is found. In addition, the results of the new dimensionless model were compared with the simulation process and experimental tests. From the results, it is inferred that the geometric qualities of a large scale can be predicted with a small scale by the proposed dimensionless model. Comparing the results of the dimensionless model with experimental tests shows that the proposed dimensionless model has fine precision in the determination of geometrical parameters and drawing force estimation. Moreover, to evaluate the accuracy of the proposed dimensionless model, the predicted value of the model has been compared by the experimental results. It is shown that the dimensionless ratios of geometrical parameters can significantly affect the estimation of the drawing force by the proposed dimensionless model, but based on similarity law, because of the constant value of these dimensionless parameters in different scales, they could not be used for dimensionless analysis separately. It is also inferred that because of the effect of contact area on the coefficient of friction, which is changed by scale changing, the only dimensionless parameter that can significantly change the drawing force is the coefficient of friction. Finally, it is shown that the dimensionless geometrical parameter and the coefficient friction should be combined for dimensionless analysis.

scholarly journals Modelling Anisotropic Phenomena of Friction of Deep-Drawing Quality Steel Sheets Using Artificial Neural Networks

2021 ◽  
Vol 21 (3) ◽  
pp. 31-42
Author(s):  
Tomasz Trzepieciński ◽  
Hirpa G. Lemu ◽  
Łukasz Chodoła ◽  
Daniel Ficek ◽  
Ireneusz Szczęsny
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Coefficient Of Friction ◽  
Multilayer Perceptron ◽  
Deep Drawing ◽  
Sheet Material ◽  
Rolling Direction ◽  
Steel Sheets ◽  
The Coefficient Of Friction ◽  
Artificial Neural

Abstract This paper presents a method of determining the coefficient of friction in metal forming using multilayer perceptron based on experimental data obtained from the pin-on-disk tribometer. As test material, deep-drawing quality DC01, DC03 and DC05 steel sheets were used. The experimental results show that the coefficient of friction depends on the measured angle from the rolling direction and corresponds to the surface topography. The number of input variables of the artificial neural network was optimized using genetic algorithms. In this process, surface parameters of the sheet, sheet material parameters, friction conditions and pressure force were used as input parameters to train the artificial neural network. Some of the obtained results have pointed out that genetic algorithm can successfully be applied to optimize the training set. The trained multilayer perceptron predicted the value of the friction coefficient for the DC04 sheet. It was found that the tested steel sheet exhibits anisotropic tribological properties. The highest values of the coefficient of friction under dry friction conditions were registered for sheet DC05, which had the lowest value of the yield stress. Prediction results of coefficient of friction by multilayer perceptron were in qualitative and quantitative agreement with the experimental ones.

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.

scholarly journals Deep Drawing Test of Vinyl Polymers and Copolymers

1971 ◽  
Vol 28 (319) ◽  
pp. 893-897,941
Author(s):  
Katsuhiro Maeda ◽  
Akira Kobayashi
Keyword(s):  
Deep Drawing ◽  
Vinyl Polymers ◽  
Drawing Test ◽  
Deep Drawing Test

Development of Metal Sheets Hydraulic Deep Drawing Tooling

2013 ◽  
Vol 706-708 ◽  
pp. 1286-1289
Author(s):  
Xian Chang Mao ◽  
Si Li
Keyword(s):  
Deep Drawing ◽  
Hydraulic System ◽  
Hydraulic Control ◽  
Blank Holder ◽  
Single Action ◽  
Drawing Test ◽  
Metal Sheets ◽  
Deep Drawing Test ◽  
Hydraulic Control System ◽  
Oriented System

A simple and practical hydraulic deep drawing test tooling is developed to investigate metal sheets formability. It is composed of two parts including the forming die and hydraulic control system. Experiments with varied material and dimensional sheets of hydraulic deep drawing and mechanical deep drawing can be fulfilled by the tooling on a Single-Action Press without complicate external hydraulic system, blank holder system and oriented system for punch, respectively. The tooling is testified to be simply, facile and reliable, which can perfectly perform the sheets forming in hydraulic deep drawing, and the formability of sheets was improved effectively with hydraulic deep drawing.

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