1. Shalaka S Kulkarni
Department of Mechanical engineering
Design of Experiments (DOE)
Using The Taguchi Approach

2. Dr. TAGUCHI IN QUALITY ENGINEERING
• Measuring cost of quality.
• Loss Function
• Consistency of performance
• Reduced variation around the
target.

3. TAGUCHI’S METHOD
• Focus on PARAMETER DESIGN
• Off-line Quality Control
• Quality Loss Function
• Signal To Noise Ratio(s/n) For
Analysis
• Reduced Variability, a Measure
Of Quality

4. TAGUCHI’S NEW PROCESS PLAN FOR
IMPROVEMENT IN PRODUCT QUALITY

5. WHAT IS AN EXPERIMENT?
Systematic procedure carried out under
controlled condition in order to
• Discover an unknown effect
• To illustrate a known effect
• Test or establish a hypothesis

6. DOE
• It all began with R. A. Fisher in England back
in 1920’s.
• Fisher wanted to find out how much rain,
sunshine, fertilizer, and water produce the
best crop.

7. • Many factors/inputs/variables must be taken into consideration
when making a product especially a brand new one
– Ex. Baking a new cake without a recipe
• The Taguchi method is a structured approach for determining the
“best” combination of inputs to produce a product or service
– Based on a Design of Experiments (DOE) methodology for determining
parameter levels
• DOE is an important tool for designing processes and products
– A method for quantitatively identifying the right inputs and parameter
levels for making a high quality product or service
• Taguchi approaches design from a robust design perspective

8. 3 Aspects which are to be analyzed by
designed experiment
1. Factor/Input
1. Controllable – Ingredients of cake and oven
2. Uncontrollable- Noise factors
2. Level- Include the oven temperature setting
and the amounts of Butter, Sugar, Milk, flour,
and eggs for making cake.
3. Responses- Taste, Consistency, moisture and
appearance of the cake.

9. Control factors with their levels
Factors Level-1 Level-2
A: Egg
B: Butter
C: Milk
E: Sugar
D: Flour
A2A1
B1 B2
C1 C2
E2E1
D1 D2

10. A1 B1
C1 D1 E1
Condition #1
A1B1C1D1E1
E2
A1 B1
C1 D1
Condition #2
A1B1C1D1E2
Experimental Conditions

11. A1 B1
C1 E1
Condition #3
A1B1C1D2E1
D2
E2
A1 B1
C1
Condition #4
A1B1C1D2E2
D2
Experimental Conditions

12. Condition # 5 through 30 . . . . .
E1
Condition #31
A2B2C2D2E1
D2
E2
Condition #32
A2B2C2DE2
D2C2
C2
A2 B2
A2 B2

13. Experiment Design Using L-8 Orthogonal Array

14. Analysis Using S/N ratio
• Used to analyze the experimental data.
• Determines the optimal parametric combinations
• Each response variable is analysed using s/n ratio
and a combination of process parameter is found.
• This gives the optimum value of respective
response variable
– Smaller the better.
– Larger the better.
– Nominal the better

15. Exp. No EGG BUTTER MILK FLOUR SUGER TASTE MOISTURE SNR1 SNR2
1 3 1 2 1 1 4 3 12.0412 9.5425
2 3 1 2 3 2 5 4 13.9795 12.0412
3 3 2 4 1 1 3 4 9.5425 12.0412
4 3 2 4 3 2 2 2 6.0206 6.0206
5 6 1 4 1 2 5 3 13.9795 9.5425
6 6 1 4 3 1 4 3 12.0412 9.5425
7 6 2 2 1 2 2 4 6.0206 12.0412
8 6 2 2 3 1 4 5 12.0412 13.9795

16. Purpose
• Significance of inputs
– What are the significant factors beyond flour, eggs, sugar
and baking?“
• Comparing alternatives
– Makes a decision which evaluate both quality and cost.
• Optimal process output.
– What are the necessary factors, and what are the levels of
those factors, to achieve the exact taste and consistency of
Mom's chocolate cake?

17. • Reducing variability
– Can the recipe be changed so it is more likely to
always come out the same?"
• Improve robustness
– Can the factors and their levels (recipe) be
modified so the cake will come out nearly the
same no matter what type of oven is used?"

18. TAHNK YOU!!!!