Optimization of Casting
Process Design with
SOLIDCast® and HyperOpt®
What is OPTICast®?
OPTICast® is a new software program from Finite
Solutions, Inc., developers of the SOLIDCast®
Solidification Modeling System (formerly sold as
AFSolid 2000).
OPTICast® works in combination with SOLIDCast® to
automatically modify a casting process design until
an optimum condition is reached. This allows the
design engineer to develop and submit an initial
process design, then let the computer find a final
design which maximizes both quality and yield.
What makes OPTICast® work?
OPTICast® uses the HyperOpt® system from Altair
Engineering, Inc. to find the optimum design.
HyperOpt® is a world-class optimization engine which
is used by the major automobile companies, among
many others. As an example, HyperOpt® was recently
applied to a sheet metal stamping to reduce the initial
blank size by 9% and saving the manufacturer over
$40,000 in material savings.
Now, for the first time, HyperOpt® has been applied to
the casting process.
What is Optimization?
Optimization is a mathematical method for finding the
“best” solution to a given problem.
Optimization allows us to automate the search for a
design solution, freeing the engineer’s time to work
on other issues, and providing a more thorough and
repeatable design process.
So, how does Optimization work?
Optimization requires that you first develop an
initial design. For example, this would typically be
an initial design for gating and risering the casting.
Based on this design, you then need to select three
elements:
• Design Variables
• Constraints
• An Objective Function
Design Variables
These are things that are allowed to vary when the
computer is searching for an optimum process
design.
A typical example would be the height and diameter of
a riser.
Other examples might be pouring temperature,
pouring time or mold preheat temperature.
A feature on the casting (such as a pad or rib) might
also be designated as a design variable.
Constraints
A constraint is some aspect of a design that
determines whether that design is acceptable or not.
For example, porosity level might be a constraint.
Since SOLIDCast® can predict the level of porosity in
a casting, you might specify a certain level as being
the maximum allowable. Any designs which result in
higher-level porosity will be rejected.
Process yield might also be a constraint. If the
foundry has a target level for yield, the system can be
instructed to reject any designs which produce a yield
below the target level.
The Objective Function
The Objective Function is the single result which you
are trying to either maximize or minimize.
For example, one possible Objective Function might
be minimization of porosity in the casting.
Another might be maximization of yield.
Still others might be minimization of total
solidification time, maximization of cooling rate, or
minimization of predicted microporosity.
The Optimization Process
Once you have defined your Design Variables,
Constraints and the Objective Function, then
OPTICast® takes over. The system begins running a
series of simulations, varying the design with each
simulation, until it is satisfied that the desired
objective has been achieved. At that point, the
optimization is complete and OPTICast® reports to
you the combination of design variables which best
satisfies your objective.
A Simple Example
Let’s look first at a very simple example – a casting
with a single top riser.
In this example, we
have imported a
casting model from
CAD, and then created
an initial riser design
as a cylindrical top
riser within
SOLIDCast®.
Having selected the
casting alloy, the mold
material and the type of
riser sleeve, we next
mesh this model. This
is a typical step in
SOLIDCast® prior to
running any simulation.
Now, we tell the system
that we want to
optimize this casting
by selecting Create
New Optimization
Project from the Mesh
menu.
This creates a
blank Optimization
Project. All we
have to do is fill in
the blanks.
First, we select the
shapes which
comprise the riser,
and designate
these as a Design
Variable by
clicking on the
“Add Variable”
button.
The Vertical Scale and
the Horizontal Scale of
the riser are now
Design Variables. In
this example, we are
allowing these to vary
up to 1.5 times or as
low as 0.5 times our
initial design.
Now we need to select a “Pin Point”.
This is the attachment point of the
riser to the casting. This point will
remain at a constant position while
the dimensions of the riser are
scaled up or down.
To select the Pin Point, we can “hide”
the casting and click on the bottom
center…
… of the riser. This establishes our
attachment point for this geometric
feature.
For this example, we have only one
riser to design. This means that we
have two design variables: the
height and the diameter of the riser.
We could also select other items of
process data such as the fill time or
the initial temperature of the casting
or mold materials as design
variables. (In this simple case we
won’t select these.)
Now we may want to specify a
constraint. As you can see, there are
numerous items that we could pick.
Here, we have selected Material
Density (a measure of shrinkage
porosity) as a constraint, with a
minimum value of 0.994. If we can
achieve this, our casting will be
substantially free from shrinkage.
Finally, we select an Objective
Function. In this case, we have
elected to maximize the yield. In
effect, we’re telling the system to find
the smallest riser which produces a
sound casting (no shrinkage).
Now that we’ve
set up our
optimization run,
all we have to do
is select Start
Optimization Run
from the menu.
OPTICast® will
now begin
running a series
of simulations,
varying the design
variables until the
smallest feasible
riser is found.
Later, when the Optimization
Run is complete…
… we can view the
results. OPTICast®
can display a series
of graphs to show us
how it arrived at the
final result. To view
the graphs, we select
View Graphs from the
menu.
The first graph
shows the
value of the
Objective
Function for
each
simulation
which was run.
In this case, we
started with a
yield of about
82% and ended
up with a yield
of 86.7% after 6
simulations
were run.
This graph
shows the
values of the
constraint
(Material
Density) for
each run.
The final
value was
0.9999, which
indicates a
sound
casting.
Here the
system has
plotted the
values which
it tried for the
Vertical Scale
(the height)
of the riser.
The final
value was
80% of the
original
value.
And a final
plot shows
the
Horizontal
Scale values
which were
tried. The
riser ended
up about
91% of its
original
diameter.
Another way to view
results is to select
View Iteration Data
from the menu…
… which brings up
an Excel spreadsheet
that shows what
happened in each
successive
simulation that was
run by OPTICast®.
The result?
Process yield was increased about 5%, and quality was
maximized, in 6 simulations run automatically by OPTICast®.
Now, let’s look at a more
complex example.
This is a large steel casting, imported from a CAD system.
Typically, we might first run a simulation of this casting with
no gates or risers, to see what the “natural” order of
solidification might be. This helps us decide where to place
gates and risers.
We can examine the final temperature distribution…
… the Progressive Solidification…
… or an “X-Ray View” showing molten metal during
solidification. By looking at these plots, we decide on an
initial design of gates and risers to produce this casting.
Based on this information, we establish a rigging design for
this casting as shown below. The next step is to create an
Optimization Project for this casting.
First, we designate the end riser as
Riser 1, and specify to the system
that this is a Design Variable.
Next, we select Riser 2…
… and Riser 3 …
… and Riser 4 …
… and finally, Riser 5. Each of
these risers will be allowed to
independently vary its height and
diameter.
We can easily
establish the “Pin
Point” (the
contact point) for
each riser by
hiding the casting,
rotating the view,
and then clicking
on the riser
contact point to
establish the
(x,y,z) coordinates
of the Pin Point.
Now we specify a Constraint. In this
case, we select Material Density
(macroporosity) and set a minimum
value of 0.994.
Finally, we select Yield Maximization as
the Objective Function.
Selecting Start Optimization Run from the
menu will begin the automatic process of
optimizing this design.
When the Optimization Run is
complete…
… we can view the results by first plotting the Objective
Function. Here, the yield started at about 48% and reached
about 78% after 100 cycles.
Note how the system initially got the yield up into the range of
60%-70%, and then found a way to increase it to 78%.
Plotting Material Density shows the soundness of the casting in
each design cycle. The final value was 0.9954, which was above
the specified constraint.
We can view the progress that OPTICast® made in deciding the
size of each riser. Here, we have plotted the progressive
changes in the height of Riser 1.
And here are the progressive values showing the diameter of
Riser 1. We can plot the values for all of the risers this way…
… or we can view a spreadsheet showing all of the values for all
of the designs investigated.
The final design is available to load and view as a model in
SOLIDCast®. Notice the reduction in size of each of the risers.
The final view shows a plot of Material Density (shrinkage). With
the design as given by OPTICast®, shrinkage is confined to the
risers, and the casting appears sound.
The Final Result?
The process yield was increased from 48% to 78%,
with all five risers individually designed to produce a
sound casting – with no operator intervention!
Some Questions
Why did it take 100 iterations?
The number of runs is roughly the square of the
number of design variables. In this case, we had the
height and diameter of five risers as design variables,
so there were 10 total design variables. (10x10 = 100)
We could have reduced the number of design
variables to 5 if we had held the riser height constant
and allowed only the diameters to vary.
More Questions…
How long did this optimization take?
On a 500-MHz PIII computer, this run took 10 hours
(six minutes per simulation). This time would have
been less than 3 hours on a 1.7-GHz P4 computer.
The number of nodes used in each simulation was
about 250,000.
More Questions…
What processes can I use OPTICast® for?
Any process that can be simulated with SOLIDCast®
can be optimized with OPTICast®. This means that
green sand, chemically-bonded sand, permanent mold
and investment processes, in ferrous and non-ferrous
alloys, can all be optimized.
More Questions…
Are there any special considerations for optimizing a
casting process design?
In general, you should use the minimum number of
nodes possible in order to reduce processing time.
Also, be aware that OPTICast® is changing the size of
features in the model, so if there is a possibility that
some features may overlap, shape priorities must be
set properly.
Non-casting material (gates, risers, feeders) must
be…
Special considerations (Cont’d):
…created with “Riser” material in the SOLIDCast®
model. This means that these components need to be
separate shapes in the model.
The number of design variables should be kept as low
as possible to reduce the number of runs. OPTICast®
has a special mode (called a Parameter Study) that
allows you to check how much a specific design
variable influences the outcome – so you know
whether it’s worth designating this as a DV.
More Questions…
What can I use as design variables?
Any geometric feature (riser, gate, feeder or casting)
that is a separate shape in the SOLIDCast® model can
be designated a design variable. Also, the initial
temperature of the casting alloy or any material can
be a DV.
More Questions…
What can I use as a constraint?
You can specify a minimum value for Yield, Material
Density, Temperature Gradient, Cooling Rate, Niyama
Criterion, and Hot Spot Criterion.
You can specify a maximum value for Solidification
Time (just in the casting, or in casting and risers),
FCC Criterion and Critical Fraction Solid Time.
You can select more than one constraint for an
optimization run.
More Questions…
What can I use as an objective function?
The objective function can be Yield, Material Density,
Temperature Gradient, Cooling Rate, Niyama
Criterion, Hot Spot Criterion, FCC Criterion,
Solidification Time, or Critical Fraction Solid Time.
You can tell the system to maximize or minimize the
objective function.
Only one objective function can be specified for each
optimization run.
What kind of cost reduction is
possible with OPTICast®?
A few examples…
Cost Savings
Example 1:
250 pound steel casting
100 castings produced per month
10% yield improvement
Annual Savings:
$ 21,240
Cost Savings
Example 2:
8 pound permanent-mold aluminum casting
3600 castings produced per month
5% yield improvement
Annual Savings:
$ 3,670
The next generation of
Solidification Modeling
Where can I find more
information?
Contact Finite Solutions, Inc:
Dave Schmidt
Phone: 847-398-5162
Email: [email protected]
Larry Smiley
Phone: 513-821-5220
Email: [email protected]