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

1. of
EXPERIMENT
Design
Dr. Archina Buthiyappan
archina.buthiyappan@um.edu.my

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
• Maximum realistic 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 uncontrol Factors )
• 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 impact on
output at different factor levels
 Qualitative or Quantitative
 Qualitative
 Type of material, Type of Column
 Quantitative
 Temperature, Voltage, Load

11. Selection Guide
Design No of Factors Levels
1 Way ANOVA 1
Factorial Design (Randomized)
2 Level Factorial 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 of Factors Levels
Miscellaneous
Resolution V Irregular Fraction Design 4- 11 2
Plackett-Burman Design 2-47 2
Taguchi OA Design Orthogonal
array designs –
L4 –L64
2

13. Selection Guide
Design No of Factors Levels
Factorial Design (Split Plot)
Regular Two-Level Design 2-15 2
Multilevel Categoric Design 2- 12 Different
level
Optimal (Custom) Design 2- 30 (Category) 2

14. Selection Guide
Design No of Factors Levels
Response Surface (Randamized )
Central Composite Design NF =2- 50
CF = 0- 10
5
Box-Behnken Design 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
What are you supposed to do before you start designing
your experiment with Design Expert?
1. Choose your Operating Parameters
2. Decide on your range (min and max)
3. Identify the appropriate design for your research

17. Design- Expert Software
What are you supposed to do before you start designing
your experiment with Design Expert?
1. Choose your response
2. Select your factors to be investigated
3. Select level of each factors (minimum and maximum
values)
4. Identify the appropriate design for your research

18. Design the experiment -RSM
Part 1
1. Select the program
2. Click the blank Sheet icon
3. Click RSM
4. Choose Central Composite Design (CCD)
5. Select the ‘numerical factors’ (if you have 3 factors, then
you have to click 3)
6. Insert the details for low and high levels.
7. Complete response form
8. Click finish and save your file

19. Design the experiment -RSM
Part 2
Enter the Response Data

20. Part 3 – Analyze the data
1. Click analysis
2. Then the response
3. Click fit summary tab (top of the screen)
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
Quadratic 0.00010 0.63799 0.98570 0.9722 Suggested
Cubic 0.84890 0.28939 0.98125 0.8555 Aliased
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. Part 3 – Analyze the data
ANOVA
1. P-values less than 0.05- model is significant , greater
than 0.1 the model is not significant
2. If too many insignificant , model reduction may
improve the model
3. Non significant Lack of fit is good– Model is fit
4. Adequate Precision – greater than 4 ( can use to
navigate the design space )
Model : p< 0.05 (Significant )
Lack of fit : p> 0.05 (Not Significant) – compares Residual error with ‘Pure Error’

22. Part 4 – Examine model Graph
1. Model graph
2. 2D Contour or 3D Surface Plot

23. Part 5 – Numerical Optimization
1. Maximize, minimize, target , in Range or Equal to
2. Running the optimization – click Solution
3. Choose the satisfactory solution
4. Do the Confirmation Run
5. Validate the Experimental Result with the Predicted
Values

24. Thank You- archina.buthiyappan@um.edu.my -