Design of experiments using Moldflow Analysis.

1. Design of E
D i f Experiments
i t
using Moldflow.
g

2. What is DOE ?
DOE – D i Of E
Design Experiment
i t
Design of Experiment is a systematic approach for evaluating
the relationship between the input factors & quality criteria
using statistical methods.
Why to use DOE?
A DOE analysis will provide you with information about the
sensitivity of the input parameters about a given part
design.

3. Type of DOE Analyzing Methods: -
T f A l i M th d
• Taguchi
Screening Analysis
•F t i l
Factorial
Full Factorial Analysis
• Taguchi & Factorial
Runs Taguchi analysis to determine the primary
factors which will be used for the factorial Analysis

4. Input Parameters for DOE Analysis: -
Input Parameters which can be used for Running DOE Analysis in
I tP t hi h b df R i A l i i
Moldflow: -
• Mold Temperature
• Melt Temperature
• Injection Time
• Injection Profile Multiplier
• Thickness Multiplier
• Packing Time (only for DOE Flow)
• Packing Profile Multiplier (only for DOE Flow)

5. Possible reasons to run a DOE Analysis : -
P ibl t A l i
To optimize the wall thickness of the part.
To indentify the process parameters to keep the shear rate within the
recommended limit while maintaining the shrinkage variation in the
p
component.
To determine the molding conditions to keep clamp force within the
maximum machine limit.
To determine interrelation between Volumetric Shrinkage & packing
time.
To determine how to solve problem of warpage caused due to
differential Shrinkage
Shrinkage.
To optimize cycle time considering part weight.

6. Taguchi Analysis : -
Taguchi analysis will filter out the main factors from large number of
factors which mostly affects the quality of the product. This is called
as screening analysis. With this method, the parameters are ranked
as per the effect on the final part quality.
When the screening analysis is complete it shows the weighting for
each of the quality criteria : -
• Flow Front Temperature
• Shear Stress
• Injection Pressure
• Overall Quality

7. Procedure to run the Taguchi Analysis: -
P d t th T hi A l i
Steps to Run Taguchi Analysis: -
Select the Analysis sequence as DOE (Fill)
Then t th
Th set the process parameter to mid-range.(Page1)
t t id (P 1)

8. Procedure to run the Taguchi Analysis: -
P d t th T hi A l i
Setting for page 2
Select the Experiment type to Taguchi & then
Set the range to analyze for the respective parameter
as shown in the figure
figure.
Then Run the analysis.

9. Screening Analysis Results: -
S i A l i R lt
Once the screening analysis is completed,
MPI shows the DOE: weighting as shown
in the figure
for each of the quality criteria (factor).
From these results we need to list out the
Vital
Vi l parameter which affect the quality of
hi h ff h li f
the product. These vital parameters will be
considered as input while running
Factorial Analysis.

10. Factorial Analysis : -
The Vital process p
p parameters which are derived
from the results of screening analysis are used as
the input, while running the factorial analysis. In our
case the vital parameters which are affecting part
quality are : -
Melt Temperature.
Global thickness multiplier.
Mold wall temperature.

11. Procedure to run the Factorial Analysis: -
P d t th F t i l A l i
Steps to Run Factorial Analysis: -
Select the Analysis sequence as DOE (Fill)
Then set the DOE Experiment type to Factorial
Set the range, to analyze for the respective parameter as
shown in the figure.
11

12. Procedure to run the Factorial Analysis: -
P d t th F t i l A l i
Rank the quality criteria based on the
results from Taguchi analysis as shown in the figure.
Then run the analysis.
After running the analysis moldflow will run
Various iteration considering various combinations,
To get the optimized parameters as shown in the adjoining figure
figure.

13. Factorial A l i R
F t i l Analysis Results: -
lt
Considering the three most effective parameters which are
figured out from the screening analysis, it launches a set of
experiments to determine the input factor for the quadratic
function of the response surface methodology.

14. Factorial A l i R
F t i l Analysis Results: -
lt
Plot shows the XY Plots for flow front temperature & Injection pressure by
which locking one of the factor y can see how it affects the q
g you quality.
y

15. Factorial A l i R
F t i l Analysis Results: -
lt
While reviewing the factorial results plots we need to see
at the response curves.
Shallow or flat response curve
The larger the variation
the steeper will be the
slope & more sensitive
Steep response curve
will be the factor
factor.

16. Taguchi & then Factorial Analysis : -
In “taguchi then factorial” analysis, moldflow runs the
g y ,
taguchi analysis & then identifies the vital factors & use it for
running factorial analysis.
Response Su ace ( et od) object e The e pe e is
espo se Surface (method) objective: e experiment s
designed to allow us to estimate interaction and even
quadratic effects, and therefore give us an idea of
the (local) shape of the response surface we are investigating.
For this reason they are termed response surface method
reason,
(RSM) designs.
RSM designs are used to:
Find improved or optimal p
p p process settings
g
Troubleshoot process problems and weak points
Make a product or process more robust against external and
non- controllable influences. "Robust" means relatively insensitive
to these influences.
influences

17. Procedure to run the Taguchi then Factorial Analysis: -
Steps to Run Taguchi then Factorial Analysis: -
Select the Analysis sequence as DOE (Fill)
Then set the DOE Experiment type to Taguchi then Factorial
& set the number of factors .
Set the delta value for the parameters & also rank quality criteria
based on results f
b d lt from ttaguchi analysis & th run th analysis.
hi l i then the l i

18. “Taguchi then Factorial” Analysis Results: -
“T hi th F t i l” A l i R lt
Screen output for the Analysis: -
Moldflow will run various iterations
considering the quality criteria
specified. If with some processing
condition there are chances to get
short molding, then it will adjust the
parameter & re-run the iteration.

19. “Taguchi then Factorial” Analysis Results: -
“T hi th F t i l” A l i R lt

20. Conclusion: -
DOE is a good tool to understand the inter-relation
between the parameters & the quality of the component.
DOE will tell you which factor needs to be controlled to get
good quality product.
It will help you to reduce the process variations.
Process Variation observed Process Variation reduced

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LTD.
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