Design of Experiment

Design your experiment using DOE to obtain maximum information using minimum number of experiments

1. of
EXPERIMENT
Design
Dr. ArchinaButhiyappan
[email protected]

2. Research Process
1.Formulating the Research Problem
2.Extensive Literature Survey
3.Developing the Research Hypothesis
4.Designing the Research
5.Collecting the Research Data
6.Statistical Analysis of Data
7.Interpretation
8.Result Presentation
9.Report/Paper Writing

3. Approaches to Experimentation
1.Trial and Error Method
•Multiple attempts are made to reach a solution
2.One Factor at a Time (OFAT)
•One factor change at a time while others are kept
fixed
3.Design of Experiments (DOE)

4. Design of Experiment (DoE)
•Statistical techniques for improving process/product
designs
•Maximumrealistic information with the minimum
number of well designed experiments
•Example of software
Design Expert
Minitab

5. Why DOE?
Reduce time
Minimum sample size
Improve performances & Reliability
Less resources
Interaction between factors
Perform evaluation of materials and system

6. Important Terminology
•Factors
–Input variables (control Or uncontrolFactors )
•Temperature, Concentration, Contact time
•Levels
–Specific values of factors (inputs)
•Continuous or Catergorical
•Contact time (1 to 3 hours), Temp. (10 –20 ℃), Pip Height (8 –9 mm)
•Response variable
–Output of the Experiment
•Adsorption Efficiency, Tensile Strength
•Replication
–Completely re-run experiment with same input levels
–Used to determine impact of measurement error
•Interaction
–Possible interaction between two or more factors

7. Example of DOE in Real Life…
Factors Levels Responses
Variable Inputs Settings Outputs
Sugars
Beans
Grind Time
Cups
10 –50 g
Type A or B
1 to 4 min
1 to 4 min
Example of Characteristics
Taste
Bitterness

8. DOE Process
Define Problem
Determine
Objectives
Brainstorm
Design
Experiment
Conduct
experiment &
Collect Data
Analyze data Interpret results
Verify Predicted
Results

9. Type of DOE
1.One Factorial
2.Full Factorial
3.Fractional Factorial
4.Screening Experiment
5.Response Surface Analysis

10. DOE
Only one or more factors having an impacton
output at different factor levels
Qualitative or Quantitative
Qualitative
Type of material, Type of Column
Quantitative
Temperature, Voltage, Load

11. Selection Guide
Design No ofFactors Levels
1Way ANOVA 1
FactorialDesign (Randomized)
2 LevelFactorial Level NF= 2-21 2
Minimum-Run Resolution V
Characterization Design
NF= 6-50 2
Minimum-Run Resolution IV Screening
Design
NF= 5-50 2
Multilevel Categorical Design CF= 1-12 Different Level
Optimal (Custom) Design NF=2-30 2

12. Selection Guide
Design No ofFactors Levels
Miscellaneous
Resolution V Irregular Fraction Design4-11 2
Plackett-Burman Design 2-47 2
Taguchi OA Design Orthogonal
array designs –
L4 –L64
2

13. Selection Guide
Design No ofFactors Levels
FactorialDesign (Split Plot)
Regular Two-Level Design 2-15 2
Multilevel CategoricDesign 2-12 Different
level
Optimal (Custom) Design 2-30 (Category) 2

14. Selection Guide
Design No ofFactors Levels
Response Surface (Randamized)
Central Composite Design NF=2-50
CF= 0-10
5
Box-BehnkenDesign NF=3-21
CF = 0-10
3
Optimal (Custom) Design NF=1-21
CF = 0-10
2
Response Surface (Split -Plot) 5-50 2
Central Composite Design NF=2-21 5
Optimal (Custom) Design NF=1-30
CF= 0-10
2

15. Design-Expert Software
DESIGN-EXPERT
®
VERSION
12 SOFTWARE TRIAL
https://www.statease.com/t
rial/

16. Design-Expert Software
Whatareyousupposedtodobeforeyoustartdesigning
yourexperimentwithDesignExpert?
1.ChooseyourOperatingParameters
2.Decideonyourrange(minandmax)
3.Identifytheappropriatedesignforyourresearch

17. Design-Expert Software
Whatareyousupposedtodobeforeyoustartdesigning
yourexperimentwithDesignExpert?
1.Chooseyourresponse
2.Selectyourfactorstobeinvestigated
3.Selectlevelofeachfactors(minimumandmaximum
values)
4.Identifytheappropriatedesignforyourresearch

18. Design the experiment -RSM
Part1
1.Selecttheprogram
2.ClicktheblankSheeticon
3.ClickRSM
4.ChooseCentralCompositeDesign(CCD)
5.Selectthe‘numericalfactors’(ifyouhave3factors,then
youhavetoclick3)
6.Insertthedetailsforlowandhighlevels.
7.Completeresponseform
8.Clickfinishandsaveyourfile

19. Design the experiment -RSM
Part2
EntertheResponseData

20. Part3–Analyzethedata
1.Clickanalysis
2.Thentheresponse
3.Clickfitsummarytab(topofthescreen)
Sources
Sequential p -
value
Lack of Fit p-
value
Adjusted
R2
Predicted
R2
Linear 0.29235 0.00030 0.06165-0.3259
2FI 0.90496 0.00020 -0.04086-0.8062
Quadratic0.00010 0.63799 0.985700.9722Suggested
Cubic 0.84890 0.28939 0.981250.8555Aliased
Model : p< 0.05 (Significant )
Lack of fit : p> 0.05 (Not Significant) –compares Residual error with ‘Pure Error’
R2 : Near to 1
Low Standard Deviation
PRESS : Low

21. Part3–Analyzethedata
ANOVA
1.P-valueslessthan0.05-modelissignificant,greater
than0.1themodelisnotsignificant
2.Iftoomanyinsignificant,modelreductionmay
improvethemodel
3.NonsignificantLackoffitisgood–Modelisfit
4.AdequatePrecision–greaterthan4(canuseto
navigatethedesignspace)
Model : p< 0.05 (Significant )
Lack of fit : p> 0.05 (Not Significant) –compares Residual error with ‘Pure Error’

22. Part4–ExaminemodelGraph
1.Modelgraph
2.2DContouror3DSurfacePlot

23. Part5–NumericalOptimization
1.Maximize,minimize,target,inRangeorEqualto
2.Runningtheoptimization–clickSolution
3.Choosethesatisfactorysolution
4.DotheConfirmationRun
5.ValidatetheExperimentalResultwiththePredicted
Values

24. Thank You
[email protected] -