“How To Design Your Model”

DOE Software

Design of Experimentwww.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 thesoftware for the web site.

DOE Wisdomwww.launsby.co

DOEpackwww.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 interface

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 VocabularyFactor 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 .

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DOE Flowchart?

Set your objectiveSet your objective

Select Output Response

Select Output Response

Select Input Factors

Select Input Factors

Select your design

Select your design

Develop your strategy

Develop your strategy

Run Experiment

Run Experiment

Fit & Diagnose Model

Fit & Diagnose Model

Interpret Model

Interpret Model

Confirm Model

Confirm Model

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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

DEFINE THE OBJECTIVE of the investigation: e.g. “sort

out important variables” or “find the optimum conditions

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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 TitlesYour 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.

SELECT INPUT FACTORS that will be

controlled during the experiment

(design variables), and their levels or

ranges of variation.

22 Weight of your sample (g)

Weight of your sample (g)

Ratio (mol) (Buffer : Sample)

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

SELECT OUTPUT

RESPONSE

that will be measured to describe the

outcome of the experimental

runs (response variables), and examine their

precision

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Enzyme Activity

(unit/mL)

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

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

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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.

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

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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.

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

factorsin powers of two (4, 8,

16…) for up to 512 runs.

This design builder offers full and

fractional two‐level factorials for 2 to 21

factorsin 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.

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 clicktopic 1: “What type of information do you want?”

For a quick overview of these color codes, press the screen tips button (or select Tips, Screen Tips) and clicktopic 1: “What type of information do you want?”

7 Insert your factors and unit

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Insert your range here!Make sure the range is not too small or too big

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11 Insert your response here!

10Choose number of response in your study

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13 List of experiment that you design before will be shown here

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Insert your experimental

data here

Develop your strategy

Develop your strategy

Run Experiment

Run Experiment

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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

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

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Number of run

Insert list of response involved in your optimization here

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Actual Values!!!!

4 List of experiment that you design before will be shown here

5

Insert your experimental

data here

Develop your strategy

Develop your strategy

Run Experiment

Run Experiment

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Thank you…..