TITLE:
Knowledge Based Cloud FE Simulation of Sheet Metal Forming
Processes
AUTHORS:
Du
Zhou, Xi Yuan, Haoxiang Gao, Ailing Wang, Jun Liu, Omer El Fakir, Denis J.
Politis, Liliang Wang, Jianguo Lin
AUTHOR
AFFILIATION:
Du
Zhou
Department of Mechanical Engineering
Imperial College London, London, UK.
Xi
Yuan
Department of Mechanical Engineering
Imperial College London, London, UK.
Haoxiang
Gao
Department of Mechanical Engineering
Imperial College London, London, UK.
Ailing
Wang
Department of Mechanical Engineering
Imperial College London, London, UK.
Jun
Liu
Department of Mechanical Engineering
Imperial College London, London, UK.
Omer
El Fakir
Department of Mechanical Engineering
Imperial College London, London, UK.
omar.al-fakir07@imperial.ac.uk
Denis
J. Politis
Department of Mechanical Engineering
Imperial College London, London, UK.
denis.politis06@imperial.ac.uk
LiLiang
Wang
Department of Mechanical Engineering
Imperial College London, London, UK.
Jianguo
Lin
Department of Mechanical Engineering
Imperial College London, London, UK.
CORRESPONDING AUTHOR:
Dr. Liliang Wang, Tel.: +44 (0)20 7594 3648
Department of Mechanical Engineering
Imperial College London, London, UK.
KEYWORDS:
Knowledge Based Cloud FE (KBC-FE) simulation,
sheet metal
forming, hot stamping, high strength aluminum alloys, high temperature forming
limit, coated tool life prediction
SHORT
ABSTRACT:
The following paper presents a novel FE simulation
technique (KBC-FE), which reduces computational cost by performing simulations
on a cloud computing environment, through the application of individual
modules. Moreover, it establishes a seamless collaborative network between world
leading scientists, enabling the integration of cutting edge knowledge modules
into FE simulations.
LONG
ABSTRACT:
The use of Finite Element (FE)
simulation software to adequately predict the outcome of sheet metal forming
processes is crucial to enhancing the efficiency and lowering the development
time of such processes, whilst reducing costs involved in trial-and-error
prototyping. Recent focus on the substitution of steel components with aluminum
alloy alternatives in the automotive and aerospace sectors has increased the need
to simulate the forming behavior of such alloys for ever more complex component
geometries. However these alloys, and in particular their high strength
variants, exhibit limited formability at room temperature, and high temperature
manufacturing technologies have been developed to form them. Consequently, advanced
constitutive models are required to reflect the associated temperature and strain
rate effects. Simulating such behavior is computationally very expensive
using conventional FE simulation techniques.
This paper presents a novel Knowledge Based Cloud FE
(KBC-FE) simulation technique that combines advanced material and friction models with conventional FE simulations in an
efficient manner thus enhancing the capability of commercial simulation software packages. The
application of these methods is demonstrated through two example case studies,
namely: the prediction of a material’s forming limit under hot stamping
conditions, and the tool life prediction under multi-cycle loading conditions.
INTRODUCTION:
Finite
Element (FE) simulations have become a powerful tool for optimizing process
parameters in the metal forming industry. The reliability of FE simulation
results is dependent on the accuracy of the material definition, input in the
form of flow stress data or constitutive equations, and the assignment of the boundary
conditions, such as the friction coefficient and the heat transfer coefficient.
In the past few years, advanced FE simulations have been developed via
the implementation of user-defined subroutines, which have significantly broadened the capability of FE software.
The
use of such advanced FE simulations in the design of forming processes for structural
components has been investigated by both the aviation and automotive industries,
with the intention of producing lightweight structures that reduces operating
costs and CO2 emissions. Particular focus has been placed on the
replacement of steel components with lower density materials, such as aluminum
alloys and magnesium alloys. However, these alloys, especially the stronger variants, offer
limited formability at room temperature and thus complex-shaped components
cannot be manufactured using the conventional cold stamping process. Therefore, advanced high temperature
forming technologies, such as warm aluminum forming 1-4, hot stamping of aluminum
alloys 5-9 and hot stamping of high
strength steels 10, have been developed over
the past decades to enable complex-shaped components to be formed. In
general, high temperature forming processes involve significant temperature variations,
strain rate and loading path changes 11,
which would, for instance, cause inevitable viscoplastic and loading history
dependent responses from the work piece materials. These are intrinsic features of high
temperature
forming processes and may be difficult to represent using conventional
FE simulation techniques. Another desirable feature would be the ability to predict
the tool life over multiple forming cycles in such processes, since they
require low friction characteristics achieved through coatings that degrade
with each forming operation. To represent all these features via the
implementation of user-defined subroutines would be computationally very expensive.
Moreover, the development and implementation of multiple subroutines would
require excessive multi-disciplinary knowledge from an engineer
conducting the simulations.
In
the present work, a novel Knowledge Based Cloud FE (KBC-FE) simulation
technique is proposed, based on the application of modules on a cloud
computing environment, that enables an efficient and effective method of
modeling advanced forming features in conjunction with conventional FE
simulations. In this technique, data from the FE software is processed at each cloud
module, and then imported back into the FE software in the relevant consistent
format, for further processing and analysis. The development of these modules and
their implementation in the KBC-FE is detailed.
PROTOCOL:
1. Development
of a high temperature forming limit prediction model
1.1) Laser cut the specimens for formability tests from the aluminum
alloy AA6082 sheets (1.5 mm thickness) into the selected geometries 12.
1.2) Etch a grid pattern, composed of 0.75 mm diameter circular points
with a regular spacing of 1 mm, on the surface of the specimens using an electrolytic
method 13.
1.3) Manually apply graphite grease as a lubricant on the non-etched
side.
1.4) Assemble the dome test rig in a high rate hydraulic press 12. Use a 250 kN hydraulic universal testing machine.
1.5) Heat up the dome test rig to a testing temperature and set the punch
at a constant moving speed. Then initiate the test. Note: The testing
temperatures are 300, 400, and 450 °C, respectively. The testing speeds include
75, 250, and 400 mm/sec.
1.6) Stop the test at the first occurrence of necking. Note: The press
stroke (i.e. final specimen height) is set such that necking is just observed
on the formed specimen.
1.7) Measure the final specimen height using a height gauge, and
calculate the strains and maximum strain rates (the rate of change of strain
with respect to time) using an optical 3D forming analysis system. Analyze the
changes in the grid spacing to compute the strains at each point of the formed
specimen.
1.8) Ensure that the optical 3D forming analysis system includes a camera,
the formed specimen, and calibration scale bars 14. Note: The specimen is placed at the center of a turntable and enclosed
with the scale bars, and their relative positions are kept fixed for the
duration of the analysis.
1.9) Set the camera at a fixed elevation (e.g. 50 cm) and angle (e.g.
30, 50, or 70°) to the specimen, and take pictures over a complete rotation
(360°) of the turntable, in increments of 15°. Note: In the present work, three
sets of images were acquired from multiple camera elevations and angles in
order to map the strains over the entire specimen 15.
1.10) Load the images into the optical 3D forming analysis software, and
proceed to compute the strains. Do this by clicking on the ‘compute ellipses and bundle’ function, which detects the grid
points, followed by clicking the ‘compute
3D points and grid’ function which builds up the grid. Note: Calculate the strains
and visualize it in the evaluation mode.
1.11) Output the strain distributions to determine the limit strains for
each specimen based on ISO 12004 16, and plot the forming limit diagrams for different forming speeds and
forming temperatures.
1.12) Calibrate a material model for AA6082 at different temperatures from
300 to 500 °C and strain rates from 0.1 to 10 sec-1. NOTE: The material
model and its constants for AA6082 are detailed in reference 17.
1.13) Implement and unify the Hosford anisotropic yield function 18, Marciniak-Kuczynski (M-K) theory 19
and the material model in step 1.12 into an
integration algorithm so as to formulate the forming limit prediction model.
NOTE: The model is described in reference 11.
1.14) Calibrate and verify the developed model for step 1.13 using the experimental
results obtained in step 1.11.
1.15) Predict the forming limits through the verified model 11 from step 1.14. NOTE: Figure 1 shows the resulting model
predictions at different temperatures, at a forming speed of 250 mm/sec, or
equivalently, a strain rate of 6.26 sec-1.
2.
Development of an interactive friction/wear model
2.1) Perform
ball-on-disc tests for coated (disc) specimens
2.1.1) Prepare titanium
nitride (TiN) coatings on bearing steel GCr15 disc using cathode arc and
mid-frequency magnetron sputtering, with the deposition parameters given in reference 20.
2.1.2) Using a scanning
electron microscope (SEM), obtain surface/cross-section topography of the coated
sample. Measure the TiN coating thickness through the SEM images by comparing
the topography (brightness and contract) of base and coating materials. NOTE:
The experimental procedures can be found in reference 20.
2.1.3) Use a white light
inter-ferometric surface pro-filometer to obtain the surface roughness of the
sample. Place the sample under the lens and adjust
the microscope to obtain clear surface structure. Illuminate the sample and
adjust the angles of x and y axes to observe clear interference strips (which
can be monitored from the screen). Set gross deepness in the software and start
measurement. Automatically scan the sample surface and calculate the surface
roughness.
2.1.4) Evaluate the adherent
strength of the sample using a micro-scratch tester. Apply an increasing load (maximum
50 N) and a scratch distance (maximum 5 mm) on the TiN coating. Determine the
critical load causing failure of the coating and obtain the micro-scratch
curves 20.
2.1.5) Assess hardness of the
sample using a hardness indenter. Apply a static load of 20 N on the sample for
15 sec. Measure the diagonal of impression made by the indenter, and then
obtain the hardness values from the tester.
2.1.6) Conduct ball-on-disc
tests on a tribometer in an ambient environment (temperature 25
°C, humidity 30%). Use a 6 mm diameter WC-6% ball (micro-hardness 1780 HV,
abrasion strength 1380 N/cm, elastic modulus 71 GPa) as the counterpart against
the coated disc. Adjust the relative sliding speed to 5 mm/sec. Apply a normal
load of 200 N. Start the motor and record friction values using the tribometer.
Interrupt the test at 180 sec, 350 sec, 400 sec and 450 sec, respectively, to analyze
the wear track using an optical microscope 20.
2.1.7) Measure the topography
of the worn surface using a white light interferometric surface profilometer
after testing.
2.1.8)
Repeat the tests (Step 2.1.6) with different normal loads (300 N, 400 N).
2.2)
Determine the evolution of the friction coefficient until the breakdown of the
hard coating, characterized by a sharp increase in the friction coefficient.
2.2.1) Plot the evolution of
the friction coefficient against time after recording the friction values in Step 2.1.6. NOTE: The evolution of the friction
coefficient is presented in reference 20.
2.2.2) Assess the evolution
of the friction coefficient in terms of wear behavior and the associated mechanisms.
NOTE: The evolution of friction is characterized into three different stages: (i)
low friction stage, (ii) ploughing friction stage, and (iii) coating breakdown
stage 20,21.
2.2.3) Evaluate the wear
states at 180 sec by manually interrupting the test, and then analyze the wear
track using an optical microscope. NOTE: This step is to investigate the wear
debris for the low friction stage as described in step 2.2.2.
2.2.4)
Repeat Step 2.2.3 at 350 sec, 400 sec and 450 sec, respectively.
2.3) Develop the
interactive friction model
2.3.1) Characterize the
overall friction coefficient μ
by combining the initial friction μα
with the ploughing friction of hardware particles μPc (as shown in Eq.(1)) 20.
2.3.2) Combine the ploughing
friction between the ball and substrate (μPs)
with the instantaneous coating thickness (h)
to model the
coating breakdown induced sharp increase of
the ploughing friction μPc (Eq.(2)).
NOTE: In this case, μPc equals
μPs when the
remaining coating thickness is zero (indicating the complete breakdown of the hard
coating).
where λ1 and λ2
are model parameters introduced to represent the physical meaning of the wear
process. λ1 describes
the influence of large entrapped wear particles, and λ2 represents the intensity of the ploughing
friction effect, which is characterized by the slope of the friction
coefficient.
2.3.3) Use a time based
integration algorithm to obtain the evolution of the remaining coating
thickness and model the accumulated wear under varying contact conditions.
Update the coating thickness in each calculation loop by Eq. (3).
where h0 is the initial coating thickness and
2.3.4) Modify Archard’s wear law
22 (Eq. (4)) and implement it in the present model.
where K is the wear coefficient, P
is the contact pressure, v is the
sliding velocity, and Hc
is the combined hardness of the coating and the substrate.
2.3.5) Use Korsunsky’s model
to calculate the combined hardness (Eq. (5)).
where Hs is the hardness of the substrate, α is the hardness ratio between
coating and substrate and β is the influence coefficient of the thickness.
2.3.6) Represent the load
dependent parameters λ1 and
K by power law equations.
where
2.3.7) Fit the interactive friction model to the
experimental results using an integration algorithm developed in the authors’
group to determine the model parameters.
3. KBC-FE
simulation case studies
3.1) KBC-FE simulation case
study 1: prediction of forming limit under hot stamping conditions
3.1.1) Create and name a new
simulation project in the FE simulation software. Select the process as ‘Stamp hot forming’ and the solver type as ‘PAM-AutoStamp’ when saving the project.
3.1.2)
Import the door inner die by clicking on the ‘Import tools CAD’ and then ‘Import
& transfer’ the door inner ‘IGS’
geometry file into the FE simulation software graphic interface. Select the ‘Hot forming’ strategy for meshing of
tools. Name the imported object as ‘Die’.
3.1.3)
Repeat Step 3.1.2 and ‘import’ the
objects of Punch and Blankholder, respectively.
3.1.4)
Click on ‘Blank’ under the ‘Set-up’ tab. Click ‘Add blank’ in the ‘Blank editor’, and set the ‘New
object’ as ‘Blank’. Then select
the type as ‘Surface Blank’.
3.1.5)
Choose ‘Outline’ for the definition
type and import the blank shape by clicking on ‘Import from CAD file’. Define ‘Refinement’
as ‘imposed level’ and select level
1 under ‘Mesh options’. Turn off ‘Automatic meshing’ and set ‘Mesh size’ to 4 mm.
3.1.6)
Define material properties in ‘Blank
editor’. Click on ‘Load a material’
under the ‘Material’ tab. Select the
‘AA6082’ (unit: mm.kg.ms.C) material
as the material properties. Set the ‘rolling
direction’ to ‘x = 1’. Set the ‘Blank thickness’ to 2 mm, and the
blank ‘Initial temperature’ to 490
°C. NOTE: The material properties and material model are described in reference
17.
3.1.7)
Click on ‘Process’ under ‘Set-up’ tab and select the ‘+’ icon to load a new macro. Browse to
‘\Stamp\Hotforming’ and select ‘HF_Validation_DoubleAction_GPa.ksa’.
In the ‘Customize’ dialog, activate
the Blank, Die, Punch, and Blankholder. Under ‘Stages’ tab, activate Gravity, Holding, Stamping, and Quenching.
3.1.8)
Set all parameters in the ‘Objects
attributes’ under ‘Set-up’ tab to
correspond with the actual experimental setup (blank holding force = 50 kN,
forming speed = 250 mm/sec, friction coefficient = 0.1, heat transfer
coefficients 23 as
a function of gap and contact pressure).
3.1.9)
Click ‘Check’ icon to check the
simulation set-up and ensure no errors in the above settings.
3.1.10)
Click ‘Computation’ icon to start
the simulation. NOTE: The software
records 11 states during the simulation in a host computer.
3.1.11) After completion of the simulation, observe
the simulation results in the FE simulation software graphical interface, and
proceed to record a ‘script’ for an
action exporting the contour values, i.e. major strain (membrane), minor strain (membrane), and temperature of all the blank elements, for a specified simulation state.
Click ‘record’ and export contour values manually. Click ‘stop’ to stop
recording. Save the script so as to repeat the same action for all 11
simulation states.
3.1.12) Click ‘play’
icon to load the script, click ‘Do All’
to export the contour values. NOTE: For each individual contour/state, the software automatically exports the values in ‘ASCII’ files under ‘major_strain_statenumber’, ‘minor_strain_statenumber’, and ‘temperature_statenumber’, respectively.
3.1.13) Save all the exported
files to a cloud computer. Run the ‘necking
prediction model’ (i.e. cloud module code) together with all the exported
files in the cloud computer.
3.1.14) Predict the onset of necking through the use of forming
limit prediction model in the cloud computer. NOTE: This
model 11 gives users the option to run the
prediction model on an individual element or all elements of the blank.
3.1.15) Manually input the simulation details/parameters
in the ‘necking prediction model’.
Input the number of states in the simulation (state 11), total stroke of the
stamping process (157 mm), stamping speed (250 mm/sec), strain range of
interest (the element
selection criterion, e.g. strain > 0.2) and all elements. NOTE: The strain range limits the
elements for which necking may take place by setting an element criterion, e.g.
only the elements with a final major strain greater than 0.2 are selected for
further evaluation in the module.
3.1.16) After completing the module computation
in the cloud
computer, automatically save all
the data (necking prediction results) into formatted ‘ASCII’ files.
3.1.17)
Load the final state of the FE simulation results. Under the ‘Contours’ tab, click on ‘Imported’ and then ‘Scalar values’. Select the ‘ASCII’ file obtained from the above
step. Display the necking prediction results in the FE simulation software.
3.2) KBC-FE simulation case
study 2: tool life prediction under multi-cycle loading conditions
3.2.1) Create and name a new simulation project in the FE
simulation software. Select the process as ‘Standard
stamping’ and the solver type as ‘PAM-AutoStamp’
when saving the project.
3.2.2)
Import the die geometry by clicking on the ‘Import
tools CAD’ and then ‘Import &
transfer’ the U-shape die ‘IGS’
geometry file into FE simulation software graphic interface. Select the ‘Validation’ strategy for meshing of
tools. Name the imported object as ‘Die’.
3.2.3)
Repeat Step
3.2.2 to import the objects of Punch and Blankholder, respectively.
3.2.4)
Click on ‘Blank’ under ‘Set-up’ tab. ‘Add blank’ in the ‘Blank
editor’, set the ‘New object’ as
‘Blank’, and then select the type as
‘Surface Blank’. Choose ‘Four points’ for the definition type
and set the blank size to 120×80 mm2. Define ‘Refinement’
as ‘imposed level’: level 1 under ‘Mesh options’. Turn off ‘Automatic meshing’ and set ‘Mesh size’ to 1.5 mm.
3.2.5)
Define material properties in ‘Blank
editor’. Click on the ‘Load a
material’ under the ‘Material’
tab. Select the ‘AA5754-H111’ (unit:
mm.kg.ms.C) material as the material properties. Set the ‘rolling direction’ to ‘x = 1’.
Set the ‘Blank thickness’ to 1.5 mm.
3.2.6)
Click on ‘Process’ under ‘Set-up’ tab and select the ‘+’ icon to load a new macro. Browse to
‘\Stamp\Feasibility’ and select ‘SingleActioin_GPa.ksa’. In the ‘Customize’ dialog, activate the Blank,
Die, Punch, and Blankholder. Under ‘Stages’,
activate Gravity, Holding, and Stamping.
3.2.7) Set all the ‘parameters’ in the simulation to
correspond with the actual experiment setup (blank holding forces = 5, 20, 50 kN,
respectively, forming speed = 250 mm/sec, friction coefficient = 0.17).
3.2.8)
‘Check’ the simulation set-up and
ensure no errors in the above settings.
3.2.9)
Click on ‘Computation’ icon and
start the ‘Computation’ for an 11-state U-shape bending simulation in a host computer.
3.2.10) After
completion of the simulation, export ‘coordinate’
data and ‘contact pressure’ data automatically
for the work piece and tools (punch, die and blank holder) as ‘ASCII’
files (as per Steps 3.1.11 and 3.1.12).
3.2.11) Save all the exported files to a cloud computer. Run
the ‘tool life prediction module’ together
with all the exported files in the cloud computer.
3.2.12) Manually input
forming parameters in the ‘tool life
prediction module’. Input the following parameters: number of states (state
11), total stroke (70 mm), stamping speed (250 mm/sec) and initial friction
coefficient (0.17).
3.2.13) Select the
tool (punch, die, or blank holder), and then start the computation for a single
element or all the elements. NOTE: All the data required for this step have been exported
in step 3.2.10. For simplification, the numbers ‘0’, ‘1’, and ‘2’ correspond to the die, punch, and blank
holder in the module, respectively. The module will promote the user to select a
number for the corresponding tool. After the selection, e.g. ‘0’ for the die, the module will give
the user an option to calculate for a single element or all the elements. The
user needs to input ‘0’ for all elements or ‘1’ for a specified element. After
the input, e.g. ‘0’, the module
starts computation automatically.
3.2.14) After completion of the module computation in the
cloud computer, automatically save all the data (including
instantaneous remaining coating thickness and friction coefficient) into
formatted ‘ASCII’ files.
3.2.15) Load and display the remaining coating thickness and
friction coefficient for the relevant elements in the FE simulation software (as
per Step 3.1.17).
REPRESENTATIVE RESULTS:
KBC-FE simulation for necking prediction
In a hot stamping process, the use of a shape-optimized
blank will not only save
material cost but also help to reduce the presence of defects, such as
necking, cracking, and wrinkling. The initial blank shape affects the material
flow significantly during forming, and hence a sensible design of the blank
shape is critical to the success of the hot stamping process and quality of the
final products. To reduce the efforts of trial-and-error experiments to
determine the optimal blank geometry, KBC-FE simulation was proven to be a
highly efficient and effective method for minimizing the areas with necking. Using this technique,
each simulation takes approximately 2 hours, while the parallel cloud module
computation for necking prediction is completed within 4 hours.
Figure 4 shows the
evolution of the blank shape used in the hot stamping, an example of automotive
door inner component. The initial blank shape, adopted from a conventional cold
stamping process, was first used in the KBC-FE simulation. Experimental results
in Figure 4(a) show that large failure (cracking or necking) areas are visible
after the hot stamping. After one iteration of the blank shape optimization, it
can be seen in Figure 4(b) that an almost fully successful panel is formed with
much less necking, compared to using the initial blank shape. It can be seen
that there is still an indication of necking at the pockets in the top right
and left corners of the panel. After further optimization in Figure 4(c), the optimized
blank shape was finally obtained with no visible necking on the panel. The optimized
blank shape determined by the KBC-FE simulation was verified experimentally
through hot stamping trials conducted on a fully automated production line
offered by a production system manufacturer.
KBC-FE simulation for tool life prediction
Conventional FE simulations of metal forming
processes are performed for a single cycle. However, in a production
environment, multiple forming cycles are performed on a given tool, where it is
found that an increase in the number of forming cycles results in an increased
variation between the formed components. This variation during multi-cycle tool
loading is the result of changing surface topography. For example, the
multi-cycle loading of forming tools with functional coatings will lead to a coating
thickness reduction due to wear. Moreover, the breakdown of the coating will also
be influenced by forming parameters, such as the load/pressure, forming speeds,
etc. The KBC-FE technique enables the simulation of sheet metal forming processes
under multi-cycle loading conditions, which is essential for the in-service
life prediction of forming tools with advanced functional coatings.
To investigate the effects of blank holding force on the tool life, blank holding force
values of 5, 20 and 50 kN were examined for a constant forming speed of 250
mm/sec. Figure 5 shows the remaining tool coating thickness distribution with
different blank holding forces after 300 forming cycles. It clearly indicates
that the remaining coating thickness decreases with an increase in the blank
holding force.
Figure 6 shows the
pressure and remaining coating thickness distribution with blank holding forces
of 5, 20 and 50 kN,
respectively, along the curvilinear distance of the die after 300 forming cycles. Since the region A-B represents the die entrance region during the U-shape bending process, the
pressure and the relative wear distance in this region were much higher than other regions of the die. Consequently, the
wear of the coating mainly occurred in this area. There are two peak values of
coating thickness reduction at 20 kN and 50 kN that
correspond to the two peaks under the pressure. Meanwhile, the remaining
coating thickness decreases with the increase of blank holding force. The
lowest remaining coating thicknesses with blank holding forces of 5, 20 and 50 kN,
were 0.905, 0.570 and 0.403 microns, respectively, where the initial coating
thickness was 2.1 microns.
FIGURE
LEGENDS:
Figure 1: Comparison between experimental
and predicted forming limit strains at different temperatures. The forming limit strains increase as temperature rises, at a constant speed
of 250 mm/sec, or equivalently, a strain rate of 6.26 sec-1.
Figure 2: Schematic chart for
knowledge based cloud FE simulation of a sheet metal forming process. Commercial
FE simulation software, is used to run the simulation and export the results
required for the individual modules. The modules, e.g. formability, heat
transfer, post-forming strength (microstructure), tool life prediction, tool design, etc., work simultaneously
and independently in the cloud, hence
enabling the integration of cutting edge knowledge from multiple sources into
FE simulations.
Figure 3: Geometry of the work piece and tools for the U-shape
bending simulation. The tools, i.e. punch, blank holder and die, are
modeled using rigid elements. Shell elements are used for the work piece
(blank) elements.
Figure 4: Evolution of blank shape for hot stamping of a door inner panel
(displayed in FE simulation). Left:
The figures in green frames represent blank shapes at each optimization stage,
and the ones in red frames correspond to the blank shape before its
optimization. Right: Necking prediction results at each optimization stage. (a)
Initial results with large failure (cracking/necking shown in red color), (b)
Reduced failure with some necking after first stage of optimization, (c) Final
optimized blank shape with no visible necking.
Figure 5: The remaining coating thickness
distribution (displayed in FE simulation) with blank holding forces of: (a) 5 kN, (b) 20 kN, and (c) 50 kN, after
300 forming cycles at a constant stamping speed of 250 mm/sec.
Figure 6 Prediction of contact
pressure and remaining coating thickness with blank holding forces of: (a)
5 kN, (b) 20 kN, and (c) 50 kN, along the curvilinear distance of the die at a
constant stamping speed of 250 mm/sec.
DISCUSSION:
The KBC-FE simulation
technique enables advanced simulations to be conducted off site using dedicated
modules. It can run functional modules on a cloud environment, that link up
nodes from different specializations, to ensure that process simulations are
conducted as accurately as possible. The critical aspects in the KBC-FE
simulation may involve independency of the FE codes, efficiency of the
computation, and accuracy of the functional modules. The realization of each
advanced function in a module would rely on the development of a new model
and/or a novel experimental technique. For example, the forming limit module is
developed based on the new unified forming limit prediction model 11, and the friction tool life prediction module
has currently been developed by the implementation of the interactive friction
model 20.
The KBC-FE simulation
technique offers the function of selective computation, i.e. only the elements
fulfilling the selection criteria are selected for further evaluation in the
individual modules. For instance, the tool life prediction module automatically
selects the elements for which the hard coating tends to breakdown, by ranking
the wear rate of all the elements in the 1st forming cycle, thus
usually less than 1% of the elements will be selected for further tool life
evaluations under multi-cycle loading conditions. In the present research, the
tool life prediction after 300 forming cycles can be completed within 5 min.
The frictional
behavior during forming can be predicted by importing the required deformation
history data into the verified friction module 20, and then importing the discrete data
points calculated by the cloud module for each element back into the FE
software. This ensures that the advanced friction module can be used by all FE
codes, regardless of their ability to incorporate user-subroutines. Additionally,
the module could be run in parallel to further reduce the computation time.
The above two case
studies show the capabilities of the KBC-FE simulation in forming design and
tool life prediction. In case study 1, conventional (hosting) simulation is
conducted using the FE software. As the stress-strain curves are input into the
FE software through a simple look-up table, it may be difficult to fully
represent the material properties at various temperatures and strain rates
during simulation. In case study 2, the interactive friction/wear model assumed
the absence of wear particles during initial sliding, and as a result, it would
be reasonable to expect a constant initial value of friction coefficient 0.17 20. Although this model revealed the
evolution of friction distribution, the frictional behavior during a forming
process is very complicated. It is difficult to completely integrate the
complex frictional behavior from the cloud module into the FE simulation.
The KBC-FE simulation
technique enables the forming limit and tool life prediction modules
incorporated into a cloud computational environment. By conducting the relevant
tests and calibrating the models accordingly, it could be applied to forming
process simulations to consequently determine the optimal parameters for
producing a component from such alloys successfully, and with no incidences of
necking. The forming limit prediction model was developed as a cloud module
that was independent of the FE software being utilized. The module could be
applied to any FE software to assess the formability of a material during
forming, without complicated subroutines 17. By importing the relevant data into the
model, calculations could be carried out to determine whether failure would
occur, in regions of the component that the user could specify, saving on
computational resources.
As a future
technology, the KBC-FE simulation will rely on the development of dedicated and
robust internet based FE simulation software packages, which would require a
highly profitable, but completely different business mode to be established by
the software developers. In addition, dedicated internal network needs to be
built within the collaborative parties to ensure the data security and the
control reliability of the industry system.
The
financial support from Innovate UK, Ultra-light Car Bodies (UlCab, reference
101568) and Make it lighter, with less (LightBlank, reference 131818) are gratefully acknowledged. The research leading to
these results has received funding from the European Union’s Seventh Framework
Program (FP7/2007-2013) under grant agreement No. 604240, project title 'An
industrial system enabling the use of a patented, lab-proven materials
processing technology for Low Cost forming of Lightweight structures for
transportation industries (LoCoLite)’. Significant support was also received
from the AVIC Centre for Structural Design and Manufacture at Imperial College
London, which is funded by Aviation Industry Corporation of China (AVIC).
DISCLOSURES:
The
authors have nothing to disclose.
REFERENCES:
13 Electrolytic Marking [Internet]. Staford: Ostling Etchmark; c2011-2015 [cited 2016 Jan 4]. Available from: http://www.etchmark.co.uk/marking-tech/electrolytic/.
14 ARGUS - Optical Forming Analysis [Internet]. Braunschweig: GOM mbH; c2015 [cited 2016 Jan 4]. Available from: http://www.gom.com/metrology-systems/system-overview/argus/.
15 ARGUS User Manual. (GOM mbH, Germany).
16 ISO12004. Metallic materials -- Sheet and strip -- Determination of forming-limit curves. (2008).