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“How To Design Your
Model”
DOE Software
Design of Experiment
www.statease.com
This EASY TO USE software has all the major
experimental designs (general ANOVA, two-level full
and fractional factorials, three-level factorials, several
RSM designs, mixture designs, and much more).
More and more features are added with each new
version of the software.
 It has powerful graphical tools and it has been
featured in several well-known texts on DOE.
Apparently it has been WIDELY USED IN
INDUSTRY.
The web site provides excellent DOE resources and
one can download a 30-day full version from the
web site.
This software also claims that it offers PRACTICALITY
and EASE-OF-USE that is IDEAL FOR BEGINNERS
but with the computing power demanded by advanced
users.
It basically offers VERY SIMILAR FEATURES TO
DESIGN-EXPERT with the addition of the Pareto chart
and a few other minor differences.
One can DOWNLOAD A 15-DAY FULL VERSION of
the
software for the web site.
DOE Wisdom
www.launsby.co
DOEpack
www.pqsystems.com This standalone software available also
FEATURES THE USUAL CLASSICAL AND
TAGUCHI DESIGNS with a wide selection of
SCREENING DESIGNS, PROCESS
OPTIMIZATION DESIGN AND ANALYSIS
TOOLS.
 It has good graphical tools and an intuitive user
This standalone DOE software is developed by the
well-known SAS Institute, developers of the SAS
program.
This is also powerful software and provides design
choices for almost every situation. Perhaps the
additional feature that is not available in the other
software is that the user can perform custom designs
which give the experimenter the most flexibility.
Obviously, this feature is ONLY USEFUL FOR THE
EXPERT USER. Limited time free download of the
software is also available from the JMP web site.
www.jmp.com
Design Expert Version 7.1.5 or LATEST Version 8
Doe Vocabulary
Factor One of the independent variables under investigation that
can be set to desire value
K Number of factors or variables the effect of which are to be
estimated in an experimental
Level The numerical value or qualitative feature of a factor
Run The act of operating the process with a factors at certain
setting
Response The numerical result of a run
Factor Experiment Design to determine the effect of all possible combinations
across all level of the factor under study
Factorial Factorial Designed to examine k factors with the fraction of the runs
required for a full factorial
Blocking A strategy for designing experiment to provide the ability to
eliminate from the experimental error a contributor of
variability that is known but not under investigation
Why Goes For
Experimental Design?
EFFICIENCY Get
more info from fewer
experiments
FOCUSING Collect
only info that really
needed
is a useful complement to multivariate data analysis.
with designed experiments there is a better possibility of
testing the significance of the effects and the relevance
of the whole model .
1 2
DOE Flowchart?
Set your
objective
Select Output
Response
Select Input
Factors
Select your
design
Develop your
strategy
Run
Experiment
Fit & Diagnose
Model
Interpret
Model
Confirm
Model
1
3
2
4
5
6
7
8
9
Will be
explain
next
semester
Building a Experimental
Design !
DEFINE THE OBJECTIVE of
the investigation: e.g. “sort out
important variables” or “find
the optimum conditions
1
To screen and identify the
POTENTIAL PLANT
PARTS of PROTEASE
PRODUCTION from
Streblus asper by using Design
of Experiment (DOE).
Example:
Studies Of Potential Parts Of Streblus
asper (Kesinai) For Protease Production
Your Final Year Project Titles
Building a Experimental
Design !
SELECT INPUT
FACTORS
that will be
controlled during
the experiment
(design variables),
and their levels or
ranges of
variation.
2 Weight of your
sample (g)
Ratio (mol)
(Buffer : Sample)
Time of Extraction
(minutes)
SELECT
OUTPUT
RESPONSE
that will be
measured to
describe the
outcome of the
experimental
runs (response
variables), and
examine their
precision
3
Enzyme
Activity
(unit/mL)
Building a Experimental
Design !
SELECT YOUR DESIGN
CHOOSE among the available
standard designs the one that is
compatible with the objective,
number of design variables and
precision of measurements, and
has a reasonable cost
4
Screening Design
The Screening designer supplies a
list of popular screening designs
for 2 or more factors.
Screening factors can be
continuous or categorical with two
or three levels.
The list of screening designs also
includes designs that group the
experimental runs into blocks of
equal sizes where the size is a
power of two.
WELCOME! 1
START YOUR DESIGN!!
2
Choose
The
Experimental
Design
3
4
The other choices are
colored like a stoplight:
GREEN FOR GO,
YELLOW FOR
PROCEED WITH
CAUTION, and RED
FOR STOP, which
represent varying
degrees of resolution: ≥
V, IV, and III,
respectively.
This design builder
offers full and
fractional two‐level
factorials for 2 to 21
factors
in powers of two (4, 8,
16…) for up to 512
runs.
The choices appear in color on your screen. White
squares symbolize full factorials requiring 2k
runs for k (the number of factors) from 2
to 9.
For a quick overview of these color codes, press the screen
tips button (or select Tips, Screen Tips) and click
topic 1: “What type of information do you want?”
7 Insert your factors and unit
8
Insert your range here!
Make sure the range is not
too small or too big
9
Factors
Numeric
Temperature
Pressure
Categoric
Catalyst
Type
Automotive
Model
11 Insert your response here!
10
Choose number of response in your
study
12
13 List of experiment that you design before will be
shown here
14
Insert your
experimental
data here
Develop your
strategy
Run
Experiment
5 6
Actual
Values!!!!
15 You might choose process factors in coded or actual
You’ve put in some work at this point so it is a good time to save it. The quickest way of
doing this is to press the standard save icon. But you can also go to the File menu and
select Save As. Type in the name of your choice for your data file.
Then click Save.
For Plackett Burman Design
minimum factor is 11.
If your factors is less than 11, you
add dummies factors to complete
it.
Next step is the same as previous.
Response Surface Design
Response Surface Methodology (RSM) is an
experimental technique invented to find the optimal
response within the specified ranges of the factors.
These designs are capable of fitting a second order
prediction equation for the response.
The quadratic terms in these equations model the
curvature in the true response function. If a maximum
or minimum exists inside the factor region, RSM can
find it.
In industrial applications, RSM designs involve a small
number of factors. This is because the required number
of runs increases dramatically with the number of
factors. The Response Surface designer in JMP lists
well-known RSM designs for two to eight continuous
factors. Some of these designs also allow blocking
Axial (or star) points, for which all but one factor set at zero
(midrange) and one factor set at outer (axial) values.
One distinguishing feature of the Box-Behnken design is that there are ONLY THREE
LEVELS PER FACTOR.
Another important difference between the two design types is that the BOX-
BEHNKEN DESIGN HAS NO POINTS AT THE VERTICES OF THE
CUBE defined by the ranges of the factors.
This is sometimes useful when it is desirable to avoid these points due to engineering
considerations. The price of this characteristic is the higher uncertainty of prediction near
the vertices compared to the Central Composite design.
CENTRAL COMPOSITE design, combines a two-level fractional factorial and two
other kinds of points:
Center points, for which all the factor values are at the zero
(or midrange) value.
Fractional
factorial
point
Axial
Point
Centre
Point
The BOX-BEHNKEN design, is an alternative to central composite designs.
Insert your optimize factor and your
range here
1
2
Number of run
Insert list of response involved in your optimization here
3
Actual Values!!!!
4 List of experiment that you design before will be
shown here
5
Insert your
experimental
data here
Develop your
strategy
Run
Experiment
5 6
Thank you…..

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dxDOE design of experiment for students.ppt

  • 1. “How To Design Your Model”
  • 2. DOE Software Design of Experiment www.statease.com This EASY TO USE software has all the major experimental designs (general ANOVA, two-level full and fractional factorials, three-level factorials, several RSM designs, mixture designs, and much more). More and more features are added with each new version of the software.  It has powerful graphical tools and it has been featured in several well-known texts on DOE. Apparently it has been WIDELY USED IN INDUSTRY. The web site provides excellent DOE resources and one can download a 30-day full version from the web site.
  • 3. This software also claims that it offers PRACTICALITY and EASE-OF-USE that is IDEAL FOR BEGINNERS but with the computing power demanded by advanced users. It basically offers VERY SIMILAR FEATURES TO DESIGN-EXPERT with the addition of the Pareto chart and a few other minor differences. One can DOWNLOAD A 15-DAY FULL VERSION of the software for the web site. DOE Wisdom www.launsby.co DOEpack www.pqsystems.com This standalone software available also FEATURES THE USUAL CLASSICAL AND TAGUCHI DESIGNS with a wide selection of SCREENING DESIGNS, PROCESS OPTIMIZATION DESIGN AND ANALYSIS TOOLS.  It has good graphical tools and an intuitive user
  • 4. This standalone DOE software is developed by the well-known SAS Institute, developers of the SAS program. This is also powerful software and provides design choices for almost every situation. Perhaps the additional feature that is not available in the other software is that the user can perform custom designs which give the experimenter the most flexibility. Obviously, this feature is ONLY USEFUL FOR THE EXPERT USER. Limited time free download of the software is also available from the JMP web site. www.jmp.com
  • 5. Design Expert Version 7.1.5 or LATEST Version 8
  • 6. Doe Vocabulary Factor One of the independent variables under investigation that can be set to desire value K Number of factors or variables the effect of which are to be estimated in an experimental Level The numerical value or qualitative feature of a factor Run The act of operating the process with a factors at certain setting Response The numerical result of a run Factor Experiment Design to determine the effect of all possible combinations across all level of the factor under study Factorial Factorial Designed to examine k factors with the fraction of the runs required for a full factorial Blocking A strategy for designing experiment to provide the ability to eliminate from the experimental error a contributor of variability that is known but not under investigation
  • 7. Why Goes For Experimental Design? EFFICIENCY Get more info from fewer experiments FOCUSING Collect only info that really needed is a useful complement to multivariate data analysis. with designed experiments there is a better possibility of testing the significance of the effects and the relevance of the whole model . 1 2
  • 8. DOE Flowchart? Set your objective Select Output Response Select Input Factors Select your design Develop your strategy Run Experiment Fit & Diagnose Model Interpret Model Confirm Model 1 3 2 4 5 6 7 8 9 Will be explain next semester
  • 9. Building a Experimental Design ! DEFINE THE OBJECTIVE of the investigation: e.g. “sort out important variables” or “find the optimum conditions 1 To screen and identify the POTENTIAL PLANT PARTS of PROTEASE PRODUCTION from Streblus asper by using Design of Experiment (DOE). Example: Studies Of Potential Parts Of Streblus asper (Kesinai) For Protease Production Your Final Year Project Titles
  • 10. Building a Experimental Design ! SELECT INPUT FACTORS that will be controlled during the experiment (design variables), and their levels or ranges of variation. 2 Weight of your sample (g) Ratio (mol) (Buffer : Sample) Time of Extraction (minutes) SELECT OUTPUT RESPONSE that will be measured to describe the outcome of the experimental runs (response variables), and examine their precision 3 Enzyme Activity (unit/mL)
  • 11. Building a Experimental Design ! SELECT YOUR DESIGN CHOOSE among the available standard designs the one that is compatible with the objective, number of design variables and precision of measurements, and has a reasonable cost 4
  • 12. Screening Design The Screening designer supplies a list of popular screening designs for 2 or more factors. Screening factors can be continuous or categorical with two or three levels. The list of screening designs also includes designs that group the experimental runs into blocks of equal sizes where the size is a power of two.
  • 16. The other choices are colored like a stoplight: GREEN FOR GO, YELLOW FOR PROCEED WITH CAUTION, and RED FOR STOP, which represent varying degrees of resolution: ≥ V, IV, and III, respectively. This design builder offers full and fractional two‐level factorials for 2 to 21 factors in powers of two (4, 8, 16…) for up to 512 runs. The choices appear in color on your screen. White squares symbolize full factorials requiring 2k runs for k (the number of factors) from 2 to 9. For a quick overview of these color codes, press the screen tips button (or select Tips, Screen Tips) and click topic 1: “What type of information do you want?”
  • 17. 7 Insert your factors and unit 8 Insert your range here! Make sure the range is not too small or too big 9
  • 19. 11 Insert your response here! 10 Choose number of response in your study 12
  • 20. 13 List of experiment that you design before will be shown here 14 Insert your experimental data here Develop your strategy Run Experiment 5 6 Actual Values!!!!
  • 21. 15 You might choose process factors in coded or actual
  • 22. You’ve put in some work at this point so it is a good time to save it. The quickest way of doing this is to press the standard save icon. But you can also go to the File menu and select Save As. Type in the name of your choice for your data file. Then click Save.
  • 23. For Plackett Burman Design minimum factor is 11. If your factors is less than 11, you add dummies factors to complete it. Next step is the same as previous.
  • 24. Response Surface Design Response Surface Methodology (RSM) is an experimental technique invented to find the optimal response within the specified ranges of the factors. These designs are capable of fitting a second order prediction equation for the response. The quadratic terms in these equations model the curvature in the true response function. If a maximum or minimum exists inside the factor region, RSM can find it. In industrial applications, RSM designs involve a small number of factors. This is because the required number of runs increases dramatically with the number of factors. The Response Surface designer in JMP lists well-known RSM designs for two to eight continuous factors. Some of these designs also allow blocking
  • 25. Axial (or star) points, for which all but one factor set at zero (midrange) and one factor set at outer (axial) values. One distinguishing feature of the Box-Behnken design is that there are ONLY THREE LEVELS PER FACTOR. Another important difference between the two design types is that the BOX- BEHNKEN DESIGN HAS NO POINTS AT THE VERTICES OF THE CUBE defined by the ranges of the factors. This is sometimes useful when it is desirable to avoid these points due to engineering considerations. The price of this characteristic is the higher uncertainty of prediction near the vertices compared to the Central Composite design. CENTRAL COMPOSITE design, combines a two-level fractional factorial and two other kinds of points: Center points, for which all the factor values are at the zero (or midrange) value. Fractional factorial point Axial Point Centre Point The BOX-BEHNKEN design, is an alternative to central composite designs.
  • 26. Insert your optimize factor and your range here 1 2 Number of run
  • 27. Insert list of response involved in your optimization here 3
  • 28. Actual Values!!!! 4 List of experiment that you design before will be shown here 5 Insert your experimental data here Develop your strategy Run Experiment 5 6