However it is very useful to go in for a fractional factorial design when the

However it is very useful to go in for a fractional

This preview shows page 32 - 38 out of 46 pages.

However it is very useful to go in for a fractional factorial design when the number of factors is large and when it is expected some factors or interactions between some factors to be unimportant. The fractional factorial experiment design is useful when main effects dominate with interaction effects being of lower order. DOE Fractional Factorial Designs
33 Taguchi Method
DOE - Taguchi Method 34 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.
Taguchi Method : When to Select a larger OA to perform Factorial Experiments 35 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
Taguchi Method Design of Experiments 36 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. 3. The number of levels that the parameters should be varied at must be specified. 4. Create orthogonal arrays for the parameter design indicating the number of and conditions for each experiment. 5. 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. 6. Conduct the experiments indicated in the completed array to collect data on the effect on the performance measure. 7. Complete data analysis to determine the effect of the different parameters on the performance measure.
Determining Parameter Design Orthogonal Array 37 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.

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• Spring '19
• Magdy Khalaf
• Fractional factorial designs, Factorial Designs