DEEP DRAWING PROCESS

Among the technological tests, the Erichsen drawing test gives a more appropriate material behavior, near the limit of the real manufactured process. In this paper an inverse finite element analysis of the Erichsen test is proposed. The new idea is to use a numerical simulation of the experimental test for the rheological identification of the constitutive equations available for sheet metals alloys. The inverse analysis is based on a robust optimization algorithm and uses simultaneously the experimental test data and the corresponding numerical one. A numerical inverse analysis software named OPTPAR was developed and improved for an automatically coupling with a commercial finite element code charged to simulate the experimental test. Results obtained for a virtual steel alloy will be analyzed numerically in order to validate the finite element model and the identification method. An application to an AA5182 aluminum alloy and a DC03 steel alloy will be presented.

Superplastic forming (SPF) is a near net-shape forming process which offers many advantages over conventional forming operations including low forming pressure under low flow stress, low die cost, more design flexibility, and the ability to shape hard metals to form complex shapes. However, low production rate due to slow forming process and limited predictive capabilities provides lack of accurate constitutive models for superplastic deformation, treated as an obstacle to the widespread use of SPF. Recent advancements in finite element tools have shown while analyzing the complex superplastic forming operations. These tools can be utilized successfully in order to develop optimized superplastic forming techniques to develop the future materials.To present the discussion mentioned above an analysis of superplastically formed front Fender car panel using HYPERFORM 9.0 software is elaborated here. Present work consist of a finite element simulations of superplastic forming of aluminum 5182 & 8090 alloy sheet in to the front fender panel of car is carried out at 460 0 C-500 0 C temperature to estimate the pressure tonnage, % thinning & major & minor strain in terms of FLD curve. Further analysis is carried out by increasing the blank holder pressure & compares the results from different pressure levels (low, medium & high), by considering better pressure level comparison of Al alloy 5182 sheet to the Al-Li alloy 8090 sheet has been explained. The major objective of present paper is to introduce the future material as a substitute in automobile industries.

Automotive stamping is a complex manufacturing process where even minor defects can lead to costly rework, scrap, and production delays. This paper introduces a backward simulation planning methodology to prevent defects by virtually "reversing time"-tracing quality issues back to their root causes and proactively adjusting processes before defects occur. Unlike traditional forward simulations that predict outcomes from given inputs, backward simulation uses desired outcomes (zero-defect targets) as a starting point and works in reverse to identify required process conditions. We integrate this approach into a digital twin of an automotive stamping line, using discrete-event production simulation and high-fidelity forming simulations. A realistic case study of a multi-stage automotive press line is presented, including complete technical calculations and a closed-loop corrective feedback system. We detail system architecture, timeline-reversal algorithms, and the coupling of simulation tools (e.g., AnyLogic for production flow, Siemens Tecnomatix for process simulation) with shop-floor data. The backward simulation identified root causes of defects (such as wrinkles and cracks) by tracing final part defects to specific press settings and material conditions upstream. A comprehensive defect prevention loop was implemented, enabling real-time feedback to adjust press parameters and scheduling. Results show a significant reduction in defect rate (from 4.0% to 1.5%), improved throughput, and cost savings of over 60% in scrap reduction, confirmed by performance metrics. Figure 10 summarizes key improvements This paper's depth and technical sophistication align with industry white paper standards, offering a structured framework-from Abstract through Conclusion-for deploying backward simulation in JIT automotive stamping. The approach demonstrates how reversing the simulation timeline, combined with real-time data, can effectively eliminate root causes of defects before they manifest, thus ensuring first-time quality in high-volume manufacturing.

The main purpose of this work is to develop an intellectualized control technique on the deep drawing of square cup made of AISI 304 DDQ stainless steel using genetic algorithm. These control methods are employed in order to investigate the most significant parameters in sheet metal forming process such as drawing force, with a view of optimizing these parameters. The genetic algorithm is used for the optimization purpose to minimize the force of the deep drawing process and to investigate the roles of other parameters as blank holder force. Experimental results show that these combinations of control system can cover a wide range of both materials and influential forming parameters automatically. The results further confirm that the developed system is effective and valid alternative for quick responsible control system with high flexibility. Key words: intelligent control, deep drawing, genetic algorithm, sheet metal forming.

This study proposes a novel technique for increasing the limiting drawing ratio (LDR) in the deep drawing process. This work uses "triple-action forming (TAF)" as a new technique to enhance LDR, achieving an LDR of 2.40 for stainless steel JIS and SUS304. The TAF consists of three sequential steps with a constant blank holder force of 3 kN. The first step involves deep drawing with a punch diameter of 30 mm. In the second step, we used a smaller punch diameter of 25 mm. The third step is going deep down with a punch diameter of 30 mm until the end of the die cavity. TAF reduces the intensity of material flow during deep drawing, which results in a delay in cup wall stretching and fracturing as a result of the reduced friction. The TAF approach offers a notable edge over alternative deep drawing techniques, such as multi-step deep drawing and temperature-based forming, due to its ability to effectively enhance the LDR. These process parameters are determined before experimentation through precise finite element simulation. Validation is shown through deep drawing experiments on JIS and SUS304, conducted using FE simulations with the Y-U model and Yoshida 6th yield function.

A blank shape optimization method is presented in this research for reduction of earring defect due to sheet anisotropy behaviour in deep drawing process by using the reduced basis technique coupled by finite element and design of experiments methods. The primary objective of the proposed method is to reduce the enormous number of design variables required to define the initial blank shape. This method is demonstrated on the blank shape optimization of deep drawing of a round cup.

The stamping processes are up to now widely utilised in mechanical industries for producing several typologies of products, ranging from computer industry to automotive components, from house products to furniture production. Anyway, the research of the best process performance for reducing the number of manufacturing steps has been very active in these last years. In fact, some interesting innovations have been introduced in this scenario: hydro-assisted processes and incremental forming operations are just pregnant examples in this direction. On the other hand, some interesting studies have been proposed also on the traditional processes, introducing some improvements aimed to extend formability limits and, finally, to reduce the manufacturing costs. In this paper an interesting simple enhancing of formability in deep drawing processes is presented. In the above process, a localised heating of the blank is obtained by using an electric heater. In addition, a cooling equipment ensures a low temperature at the centre of the sheet, in correspondence of the punch acting zone. In this way a decreasing of the material strength is obtained on the flange, causing a reduction of the axial stress on the vertical wall of the part. At the same time the material strength does not change at the bottom part, where the principal stress is applied. In this way a formability enhancing is easily obtained. An accurate justification from a theoretical point of view and a suitable prediction of the limit conditions are given by using a customised FE simulation. An experimental assessment of the analysis has been carried out by using a properly designed and manufactured equipment.

The stamping processes are up to now widely utilised in mechanical industries for producing several typologies of products, ranging from computer industry to automotive components, from house products to furniture production. Anyway, the research of the best process performance for reducing the number of manufacturing steps has been very active in these last years. In fact, some interesting innovations have been introduced in this scenario: hydro-assisted processes and incremental forming operations are just pregnant examples in this direction. On the other hand, some interesting studies have been proposed also on the traditional processes, introducing some improvements aimed to extend formability limits and, finally, to reduce the manufacturing costs. In this paper an interesting simple enhancing of formability in deep drawing processes is presented. In the above process, a localised heating of the blank is obtained by using an electric heater. In addition, a cooling equipment ensures a low temperature at the centre of the sheet, in correspondence of the punch acting zone. In this way a decreasing of the material strength is obtained on the flange, causing a reduction of the axial stress on the vertical wall of the part. At the same time the material strength does not change at the bottom part, where the principal stress is applied. In this way a formability enhancing is easily obtained. An accurate justification from a theoretical point of view and a suitable prediction of the limit conditions are given by using a customised FE simulation. An experimental assessment of the analysis has been carried out by using a properly designed and manufactured equipment.

evaluation on suitability for deep draw and roll forming process for power steering pulley. 4.2 100kg material for from 1st process to final process (Boring process) 4.3 Every process part was observed for any abnormalites. 4.4 Critical process to monitor is at OP100 (upsetting) and final process (Boring process) as these process would potentially causing defect to functional of pulleys 3 EVALUATION 5.1 OP10/20-Blank & draw

Among the technological tests, the Erichsen drawing test gives a more appropriate material behavior, near the limit of the real manufactured process. In this paper an inverse finite element analysis of the Erichsen test is proposed. The new idea is to use a numerical simulation of the experimental test for the rheological identification of the constitutive equations available for sheet metals alloys. The inverse analysis is based on a robust optimization algorithm and uses simultaneously the experimental test data and the corresponding numerical one. A numerical inverse analysis software named OPTPAR was developed and improved for an automatically coupling with a commercial finite element code charged to simulate the experimental test. Results obtained for a virtual steel alloy will be analyzed numerically in order to validate the finite element model and the identification method. An application to an AA5182 aluminum alloy and a DC03 steel alloy will be presented.

At present, sheet metal forming is gaining importance in the
manufacturing sector and this process is essentially used to produce
various engineering components having applications in railway cars,
automobile bodies, construction, farm equipment, office furniture, lighting
equipment, computer peripherals, medical appliances, etc. The formed
component having the best design, superior quality and an aesthetic look
is the demand of the present sheet metal industry. The present needs
of the sheet metal manufacturing industry are to optimise the forming
operations.
Deepdrawing is one of the sheet metal forming operations widely
used in manufacturing industries for producing cup-shaped components.
In this process, sheet metal placed between the die and blank-holder on
its periphery can be drawn into the die for forming into a cup-shaped
component. This process is extensively used for the manufacture of cups,
cans and other similar types of products. The deep drawing process is
influenced by geometric parameters as well as process parameters. Its
geometric parameters include Die Shoulder Radius (DSR), Punch Nose
Radius (PNR) and clearance between punch and die. The Blank Holder
Force (BHF), coefficient of friction, punch speed and blank temperature
are the process parameters. All these parameters have their influence on
the quality of the cup produced. A literature search reveals that PNR, and
BHFaresignificantly influencing tool design based parameters of the deep
drawing process. In any deep drawing process uniform wall thickness of
the drawn cup is the desired quality characteristic. From the literature, it
is also found that these parameters vary in their respective ranges. Experiments are conducted using DoE to optimise the
combinations of geometric and process parameters and levels that would
give minimum variation in the thickness of the cup produced in the deep
drawing process. To develop optimised deep drawing facilities using
Design of Experiments (DoE), the ranges for highly influencing parameters
are fixed after a thorough study of literature. The tool setup is designed for
optimization of highly influencingparametersusingDoEbasedonTaguchi
technique. The parameter ranges for highly influencing parameters fixed
are PNRfrom 5.5 to 10.5 mm, DSR from 5.5 to 10.5 mm and BHFfrom3
to 7 kN. The uniform thickness of the drawn cup is considered as output
characteristic and the variation in cup wall has to be as minimum as
possible. The three level values fixed for these parameters are 5.5 mm,
8 mmand10.5 mm for PNR; 5.5 mm, 8mm and 10.5 mm for DSR and
3 kN, 5kNand7kNforBHF.
Subsequently, experimental tests are conducted using L9
Orthogonal Array (OA) for optimisation of the process setup. Following
the DoE procedure, nine experiments are performed according to L9 OA
instead of conducting all the possible 27 experimental tests. During the
experimental tests, Aluminium alloy AA6111 blanks are considered as the
test material having 130 mm diameter and 0.9 mm thickness. In every test,
the thickness readings are measured at nine different points, radially from
the edge of the flange to the centre of a cup at the bottom. Considering
"Nominal is the Best" (NB) characteristic, The Signal to Noise (S/N)
ratios are determined using Qualitek- 4 software. The application of this
Taguchi based DoE software for optimisation of the deep drawing process
indicated the optimal values for the parameters considered, and these are
PNRof5.5 mm,DSRof5.5mmandBHFof7kN.Thestatistical method
Analysis of Variance (ANOVA) is applied to estimate the contribution of
each optimal parameter to obtain a minimum variation in the thickness of
the cup produced and it is found that PNR, DRS and BHF contributions
are 33.736 %, 07.140 % and 54.315 % respectively

Experiments are conducted using DoE to optimise the
combinations of geometric and process parameters and levels that would
give minimum variation in the thickness of the cup produced in the deep
drawing process. To develop optimised deep drawing facilities using
Design of Experiments (DoE), the ranges for highly influencing parameters
are fixed after a thorough study of literature. The tool setup is designed for
optimization of highly influencingparametersusingDoEbasedonTaguchi
technique. The parameter ranges for highly influencing parameters fixed
are PNRfrom 5.5 to 10.5 mm, DSR from 5.5 to 10.5 mm and BHFfrom3
to 7 kN. The uniform thickness of the drawn cup is considered as output
characteristic and the variation in cup wall has to be as minimum as
possible. The three level values fixed for these parameters are 5.5 mm,
8 mmand10.5 mm for PNR; 5.5 mm, 8mm and 10.5 mm for DSR and
3 kN, 5kNand7kNforBHF.
Subsequently, experimental tests are conducted using L9
Orthogonal Array (OA) for optimisation of the process setup. Following
the DoE procedure, nine experiments are performed according to L9 OA
instead of conducting all the possible 27 experimental tests. During the
experimental tests, Aluminium alloy AA6111 blanks are considered as the
test material having 130 mm diameter and 0.9 mm thickness. In every test,
the thickness readings are measured at nine different points, radially from
the edge of the flange to the centre of a cup at the bottom. Considering
"Nominal is the Best" (NB) characteristic, The Signal to Noise (S/N)
ratios are determined using Qualitek- 4 software. The application of this
Taguchi based DoE software for optimisation of the deep drawing process
indicated the optimal values for the parameters considered, and these are
PNRof5.5 mm,DSRof5.5mmandBHFof7kN.Thestatistical method
Analysis of Variance (ANOVA) is applied to estimate the contribution of
each optimal parameter to obtain a minimum variation in the thickness of
the cup produced and it is found that PNR, DRS and BHF contributions
are 33.736 %, 07.140 % and 54.315 % respectively

maximumpunchload is linearly proportional to the blank diameter for
all successfully drawn cups and remains constant for all oversized blanks
forming into crack induced/failed cups.
As the maximumpunch load is linearly proportional to the blank
diameter, a newconceptknownasarapidmethodofdeterminationofLDR
using only three blanks of different sizes gained significance. In the rapid
determination method, the LDR values for Aluminium alloy AA6111 and
Copper C11000 material are found. Experiments are conducted with three
different size blanks, i.e., two undersized blanks of different diameters
for determining the linearity of the punch load and one oversized blank
for finding the maximum forming load at which fracture takes place.
The LDR results found in the traditional method involving testing of
several different sized blanks and the rapid method using only three
different blank sizes are compared. The results found through the rapid
determination method of LDR are in close agreement with the traditional
method.
Further, simulation tests are conducted for Aluminium alloy
AA6111 at 30 oC temperature for confirmation of experimentally found
LDRat 30 oCtemperature. The deep drawing model is constructed using
CATIA V5 and simulations are performed using PAM-STAMP 2G®, a
simulation software. It has been observed that the experimental results at
30 oC temperature for Aluminium alloy AA6111 are in close agreement
with simulation results obtained at 30 oC temperature.
It is found through this present research that the rapid
determination of LDR is quick compared to the traditional method. This
concept of finding LDR can reduce testing time and scrap in multistage
deep-drawing operations in which two or more drawing operations are
required to form a complete cup. Results of the parameter optimisation,
the experimental tests conducted determining LDR values for Aluminium alloy AA6111andCopperC11000atdifferenttemperaturesandsimulation
tests discussed and conclusions made are presented.

This study examines the Deep Drawing Process (DDP) of square aluminum sheets, emphasizing its applications across various industries, including medical devices. The process is simulated using ABAQUS finite element software, analyzing the effects of parameters like punch speed and friction on the final results. One of the important aspects of this study is the use of the Forming Limit Diagram (FLD) criterion to determine the rupture point of the sheet during the DDP. This criterion allows us to assess the deformation of the sheet and examine the possibility of predicting and preventing rupture at different stages of the deep drawing. The results obtained from the simulations show that reducing the punch speed in the DDP of aluminum leads to a greater thickness of the final part at the same depth produced. In other words, decreasing the punch speed results in an increased duration of the DDP, which aids in producing parts with greater depth. This finding is particularly important in the manufacture of components featuring intricate shapes that demand high precision. Moreover, the research results indicate that by reducing the frictional interaction between the holder and sheet, as well as increasing the frictional interaction between the sheet and die, it is possible to produce parts with greater depth and less wrinkling and tearing. This outcome suggests that optimizing lubrication conditions and appropriately designing various parts of the die can significantly enhance the final quality of the produced components and the efficiency of the DDP.

Deep drawing process is extensively used in most of the manufacturing industries for production of components used in automobile, aerospace, beverage and domestic The range varies from simple cups to large complex aerospace components. It involves major plastic deformation of the complete ductile flat blank. It may be a single stage or multistage operation depending upon the complexity of the component produced. The number of stages depends upon the nature of the material, process parameters and tool parameters.

The process by whom a hollow cup is formed by forcing a flat blank into a die using a punch is called Deep drawing.
It involves the use of the presses or tools having a double action for hold down force and punch force.
The focused region of a deep drawn part is the flange which is under circumferential compressive and radial tension stresses.

Deep drawing is used widely to manufacture objects out of sheet metal. Several geometric parameters can generate deep drawing products that are free of defects. The radius of the Punch corner, Clearance and radius of the die corner are the three parameters that have the biggest effects on deep drawing. The stress distribution of an SS 316 cylindrical cup was examined in this study while considering these parameters. The finite element simulation of this process is performed in ABAQUS software. FEM simulation using the isotropic hardening law was carried out. Response surface methodology is used to design experiments. When the outcomes of the FEA and the experiments were compared, they agreed well. The most influential parameters were identified by ANOVA analysis. The finding showed that the optimum parameter of the punch corner radius (PCR) should be 5.3 mm, the Die Corner Radius (DCR) should be 6.6 mm, and clearance should be 1.07 mm to minimize stress distribution.

Deep drawing of sheet metal is one of the most important transformation’s process in which a sheet metal blank is drawn into a forming die via a punch force. The conventional deep drawing of sheet metal techniques, known for their wide usage in several industry sectors, are experimental and expensive processes. Numerical simulation using Finite Element Methods (FEM) offers the ability of analyzing the feasibility of a process, allowing the benefit of saving time and considerably reducing costs by comparison with traditional methods. This virtual simulation requires the realization of the tools and their adjustment according to the resulting data till seizing a satisfactory adequacy of results. In this study, a Hill48 anisotropic yield criterion was adopted to express the mechanical material properties using ABAQUS/EXPLICIT software. A finite element model is developed for the 3D numerical simulation of sheet metal deep drawing process. Two aluminum alloys AA1050 and AA1100, were stu...

Deep drawing is a widely used sheet metal forming technique, and its successful implementation has been a subject of research since many years. It has undergone many developments, one of the important ones being the application of numerical modeling techniques, like the finite element method (FEM) to simulate the process. Although deep drawing has been a subject of research for many years, there is still not much data available on deep drawing of miniature components, which find extensive application in electronics industry. The deep drawing process is affected by many material and process parameters, like the strain-hardening exponent, plastic strain ratio, friction and lubrication, blank holder force, presence of drawbeads, punch velocity, etc. This paper aims at identifying the important parameters that affect the deep drawing process and quantitatively studying the effect of these parameters on the deep drawing operation for components of similar shape but different sizes. Thus, establishing a correlation between the size of a component and the effect of the parameters on the deep drawing of the component. The study consists of a plane strain analysis of bell shaped geometry. Taguchi's robust design technique [Quality Engineering Using Robust Design, Prentice-Hall, New Jersey, 1989, p. 145] has been used to design the experiments using the maximum thinning strain developed in the walls as the quality characteristic. Since carrying out actual experiments is both expensive and time consuming, computer modeling has been used to simulate the experiments. A FEM-based program, SHEETS , developed by Wagoner and co-workers [Int. J. Meth. Eng. 30 (8) (1990) 1471] has been used for this purpose.

The paper presents an analysis of the multistage deep drawing process considering the three
deformation stages namely drawing reverse and reverse redrawing respectively. This work aim to
study the mechanism of deformation during the redrawing process where the second and the third
stages were done in reverse redrawing and study the effect of this mechanism on produced cup wall
thickness, strain distribution across the wall of the drawn part. 2-D model of cylindrical cup
(46.75mm) diameter has been developed in the first stage from sheet with thickness (0.5mm) of the
low carbon steel (AISI 1008) and (85mm) diameter, while for second and third stages of drawing a
punch diameter (32.725mm, 27.489mm) respectively, and inside diameter of dies equal to
(33.825mm, 28.589mm) respectively, the clearance is chosen for three stage equal to 0.55mm. A
commercial available finite element program code (ANSYS 11), is used to perform the numerical
simulation of the multistage deep drawing. The results show that, when considering multi-stage
drawing, the task is even more difficult because the strain and thickness distribution resulting from
the first stage will influence the subsequent results, increase in thinning in the wall cup will appear
in the second and third stages. Finally this work introduces new method (multi reverse redrawing)
to produce circular cup throw three stages of drawing reduction in one stroke without the need to
the loading and unloading the tools among the stages as in direct redrawing which means reducing
the cost, time, efforts and enhancing cup production.

The objective of this analysis is to highlight one the most encountered problems in the sheet metal forming of mild steel, namely the rupture. Cases of tear sheet, recorded in the framing workshop of a manufacturing company from where was sparked the simulation analysis of the barrow drawing operation on the finite element code Abaqus/cae, which allowed the elaboration of curves determining the stress and strain distribution, and the material behavior at the deep drawing, and to explore critical industrial problem expressed by a deep plastic deformation which leads to the appeared rupture on sheets metal. This work serves to predict the risk of necking and determines areas with high local thinning before a tear. The simulation results can be summarized in a representation of comparison curves of the stresses and strain distribution, in the different areas of the drawn part, respectively the flange, the cup wall and the punch bottom region of the wheelbarrow to locate areas with high local thinning before a tear.

Deep drawing is one of the most important sheet metal forming processes in automotive, aerospace and nuclear industries. In this process, the sheet metal blank is formed into a cup shape by an application of punch into the die. The present work is aimed at studying the formability and the nature of fracture for one of the important materials in industrial applications, austenitic stainless steel 316 at different temperatures. Circular blanks were deep drawn at room temperature, 150 and 300°C using a 20 Ton hydraulic press coupled with a furnace and found that formability of the austenitic stainless steel 316 increased as the temperature was increased. This material underwent dynamic strain aging between 350 and 550°C. Fractured surface of the broken tensile test specimen at different regions were studied and analyzed using scanning electron microscope. It was observed that the nature of the fracture was brittle in dynamic strain aging region.

Deep drawing process is a significant metal forming process used in the sheet metal forming industries. From these process most complex shapes is manufactured with less defects. Deep drawing process has many effectible process parameters from which optimum parameters must be identified so that an efficient final product will be obtained. In which effects of different sheets on deep drawing process observed for sheet metal of 0.8mm of SS304, Brass and 0.9 mm of Al. The experiments are performed by designing the deep drawing tools such as die, blank holder, and punch. Also the numerical simulations are performed for deep drawing of rectangular cups using two levels of aforesaid parameters like lubricating condition and blank material. The experiments are conducted by attaching the deep drawing tools to the Universal Testing Machine. For numerical simulation a commercial FEM code is used in which Hollomon's power law and krupowski power law along with Hill's 1948 yield criterions are implemented. The deep drawing setup used in the FEM code is modelled using a CAD tool by considering the modelling requirements from the literature. Strain path of 55x55mm are simulated. Punch forces and dome heights are evaluated for all the conditions. From this work the formability for different metal sheets is observed for different blank materials.

Deep drawing is one of the most importing processes for forming a sheet metal parts. It is widely used for mass production of good strength and light weight part in automobile and aerospace industries. Optimization of process parameters in sheet metal forming is the important task to reduced manufacturing cost per year. In this review paper importance is given to gather the recent research work and the effect of process parameter on the sheet metal component .There are many process parameters like that blank holding force, die radius, friction, punch speed, and other factors that affect on product quality produced by deep drawing process. In square shape of component major failures are occurs due to stress concentration at the square shape sharp edges of die and punch. Therefore is important to optimize the process parameters to avoid stress concentration. For the ooptimization several tools have been used by researcher like ANOVA, FEA, Taguchi etc. In this review paper major focus ...

The purpose of this study is to consider the effect of different parameters on the wrinkling phenomenon of square cups during the deep drawing process. An experimental system was prepared which consisted of a hydraulic press capable of applying different loads on the sheet metal blank and a blank holder and the process was controlled by a computer. All simulations were performed by using the ABAQUS/EXPLICIT V6.9 software, and the effect of different aluminium alloys, punch and die radius, blank holder force, blank holder gap, and friction coefficient on the wrinkling phenomenon has been investigated. Moreover, the minimal blank holder force was obtained by plotting minor strain with respect to the major strain of produced cups, and obtaining the maximum height of wrinkle in a cup. The wrinkling phenomenon was further investigated on different alloys such as A6111-T4, AA5754-O and Al-1050. The results imply that increasing the blank holder force and friction coefficient on the one ha...

Deep drawing process is a significant metal forming process used in the sheet metal forming industries. From these process most complex shapes is manufactured with less defects. Deep drawing process has many effectible process parameters from which optimum parameters must be identified so that an efficient final product will be obtained. In which effects of different sheets on deep drawing process observed for sheet metal of 0.8mm of SS304, Brass and 0.9 mm of Al. The experiments are performed by designing the deep drawing tools such as die, blank holder, and punch. Also the numerical simulations are performed for deep drawing of rectangular cups using two levels of aforesaid parameters like lubricating condition and blank material. The experiments are conducted by attaching the deep drawing tools to the Universal Testing Machine. For numerical simulation a commercial FEM code is used in which Hollomon's power law and krupowski power law along with Hill's 1948 yield criterions are implemented. The deep drawing setup used in the FEM code is modelled using a CAD tool by considering the modelling requirements from the literature. Strain path of 55x55mm are simulated. Punch forces and dome heights are evaluated for all the conditions. From this work the formability for different metal sheets is observed for different blank materials.

Among the technological tests, the Erichsen drawing test gives a more appropriate material behavior, near the limit of the real manufactured process. In this paper an inverse finite element analysis of the Erichsen test is proposed. The new idea is to use a numerical simulation of the experimental test for the rheological identification of the constitutive equations available for sheet metals alloys. The inverse analysis is based on a robust optimization algorithm and uses simultaneously the experimental test data and the corresponding numerical one. A numerical inverse analysis software named OPTPAR was developed and improved for an automatically coupling with a commercial finite element code charged to simulate the experimental test. Results obtained for a virtual steel alloy will be analyzed numerically in order to validate the finite element model and the identification method. An application to an AA5182 aluminum alloy and a DC03 steel alloy will be presented.

Deep drawing process has been an important manufacturing process to produce automotive parts of good strength and light weight. There are many process parameters and other factors that affect product quality produced by deep drawing. This paper is highlighting recent research work and results in deep drawing. Deep-drawing operations are performed to produce a light weight, high strength, low density, and corrosion resistible product. These requirements will increase tendency of wrinkling and other failure defects in the product. Parameters like as blank-holder pressure, punch radius, die radius, material properties, and coefficient of friction affect deep drawing process. So a great knowledge of process is required to produce product with minimum defects. This review paper has given the attention to gather recent development and research work in the area of deep drawing.

Hyper Form is the module of Altair Hyperworks used for formability analysis and prediction various defects like wrinkles, spring-back and thinning occurred in sheet metal drawing process. A sheet metal drawing process is widely used in industry for producing seamless cup formed parts in automobile, aircraft and household applications. During the product design and tool design designer are still adapts trial and error method to decide blank shape, blank size, draw tool and process parameters. Computer aided engineering (CAE) plays very significant role in the decision making of various parameters of sheet metal forming processes and it helps to designer during product design as well tool design stage to decide optimum and accurate process parameters. The use of CAE software such as HyperForm during product design and tool design stage minimizes the tool trial time and cost. This paper describes the study of effect of die parameter such as die radius and process parameters such as bla...