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1. Fractional Factorial Designs of Experiments P M V Subbarao
Professor
Mechanical Engineering Department
4. Most Significant Factor
5. Sample 23 Experiment
6. Why Fractional Factorials?
7. Why so many Treatments?
8. How to select a subset of 4 runs from a 23=8 -run design? Many possible “fractional” designs
9. First Choice
10. Second Choice
11. Third Choice
12. How to select a subset full factorial design
14. DOE - Taguchi Method Dr. Taguchi of Nippon Telephones and Telegraph Company, Japan has developed a method based on " ORTHOGONAL ARRAY " experiments.
This gives much reduced " variance " for the experiment with " optimum settings " of control parameters.
"Orthogonal Arrays" (OA) provide a set of well balanced (minimum) experiments serve as objective functions for optimization.
15. Taguchi Method : When to Select a ‘larger’ OA to perform “Factorial Experiments” We always ‘think’ about ‘reducing’ the number of experiments (to minimize the ‘resources’ – equipment, materials, manpower and time)
However, doing ALL / Factorial experiments is a good idea if
Conducting experiments is ‘cheap/quick’ but measurements are ‘expensive/take too long’
The experimental facility will NOT be available later to conduct the ‘verification’ experiment
We do NOT wish to conduct separate experiments for studying interactions between Factors
16. Taguchi Method Design of Experiments The general steps involved in the Taguchi Method are as follows:
1. Define the process objective, or more specifically, a target value for a performance measure of the process.
2. Determine the design parameters affecting the process.
The number of levels that the parameters should be varied at must be specified.
3. Create orthogonal arrays for the parameter design indicating the number of and conditions for each experiment.
The selection of orthogonal arrays is based on the number of parameters and the levels of variation for each parameter, and will be expounded below.
4. Conduct the experiments indicated in the completed array to collect data on the effect on the performance measure.
5. Complete data analysis to determine the effect of the different parameters on the performance measure.
18. Determining Parameter Design Orthogonal Array The effect of many different factors on the performance characteristic in a condensed set of experiments can be examined by using the orthogonal array experimental design proposed by Taguchi.
The main factors affecting a process that can be controlled (control Factors) should be determined.
The levels at which these parameters should be varied must be determined.
Determining what levels of a variable to test requires an in-depth understanding of the process, including the minimum, maximum, and current value of the parameter.
If the difference between the minimum and maximum value of a parameter is large, the values being tested can be further apart or more values can be tested.
If the range of a parameter is small, then less values can be tested or the values tested can be closer together.
Typically, the number of levels for all parameters in the experimental design is chosen to be the same to aid in the selection of the proper orthogonal array.
21. Taguchi Method : How to Select a ‘larger’ OA to perform “Factorial Experiments”
22. L27 Array
23. L 50 Array
