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5.
Process Improvement
5.3. Choosing an experimental design 5.3.3. How do you select an experimental design? 5.3.3.4. Fractional factorial designs
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| Use low-resolution designs for screening among main effects and use higher-resolution designs when interaction effects and response surfaces need to be investigated |
The basic purpose of a fractional factorial design is to economically
investigate cause-and-effect relationships of significance in a given
experimental setting. This does not differ in essence from the purpose
of any experimental design. However, because we are able to choose
fractions of a full design, and hence be more economical, we also have
to be aware that different factorial designs serve different purposes.
Broadly speaking, with designs of resolution three, and sometimes four, we seek to screen out the few important main effects from the many less important others. For this reason, these designs are often termed main effects designs, or screening designs. On the other hand, designs of resolution five, and higher, are used for focusing on more than just main effects in an experimental situation. These designs allow us to estimate interaction effects and such designs are easily augmented to complete a second-order design - a design that permits estimation of a full second-order (quadratic) model. |
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| Different purposes for screening/RSM designs | Within the screening/RSM strategy of design, there are a number of functional purposes for which designs are used. For example, an experiment might be designed to determine how to make a product better or a process more robust against the influence of external and non-controllable influences such as the weather. Experiments might be designed to troubleshoot a process, to determine bottlenecks, or to specify which component(s) of a product are most in need of improvement. Experiments might also be designed to optimize yield, or to minimize defect levels, or to move a process away from an unstable operating zone. All these aims and purposes can be achieved using fractional factorial designs and their appropriate design enhancements. | ||
