A type of designed experiment that is frequently used to determine the most important factors in a process. With a screening design, you use a relatively small number of runs with many potential factors to determine a smaller number of most important factors that affect process quality. After screening experiments, you can do optimization experiments if you need more detail. For example, say you are examining the various factors that affect ice cream texture: fat content, pasteurization temperature, homogenization process, mixing speed, draw temperature, emulsifier, stabilizer, and cooling speed. You can use a screening design to identify the most important factors that affect ice cream texture. If necessary, you can use a larger factorial design or a response surface design to optimize your process.
Plackett-Burman designs are usually resolution III, 2-level designs. In a resolution III design, main effects are aliased with 2-way interactions. Therefore, you should only use these designs when you are willing to assume that 2-way interactions are negligible. Because of this assumption, Plackett-Burman designs can have fewer runs than definitive screening designs. If you suspect an interaction after you complete a Plackett-Burman design, you can fold the design, which doubles the number of runs. After you fold a Plackett-Burman design, main effects are not aliased with 2-way interactions. For more information, go to What is folding?.
Plackett-Burman designs are usually resolution III, 2-level designs. In a resolution III design, main effects are aliased with 2-way interactions. Therefore, you should only use these designs when you are willing to assume that 2-way interactions are negligible.
Use Plackett-Burman designs to identify the most important factors in the early phases of experimentation. For example, say you are examining the various factors that affect ice cream texture: fat content, pasteurization temperature, homogenization process, mixing speed, draw temperature, emulsifier, stabilizer, and cooling speed. You can use a Plackett-Burman experiment to identify the most important main effects, use fractional or full factorial designs to study them more, then use response surface designs to optimize your process.
Mixture experiments are a special class of response surface experiments in which the product under investigation is made up of several components or ingredients. Designs for these experiments are useful because many product design and development activities in industrial situations involve formulations or mixtures. In these situations, the response is a function of the proportions of the different ingredients in the mixture. For example, you might be developing a pancake mixture that is made of flour, baking powder, milk, eggs, and oil. Or, you might be developing an insecticide that blends four chemical ingredients. For more information on mixture designs, go to
In the simplest mixture experiment, the response (the quality or performance of the product based on some criterion) depends on the relative proportions of the components (ingredients). The amount of components, measured in weights, volumes, or some other units, add up to a common total. In contrast, in a factorial design, the response varies depending on the amount of each factor.
|Simplex Lattice Degree 1|
|Simplex Lattice Degree 2|
|Simplex Lattice Degree 3|
Mixture designs that cover only a subportion or smaller space within the simplex.
The goal of an extreme vertices design is to choose design points that adequately cover the design space. The following figure shows the extreme vertices for two three-component designs with both upper and lower constraints:
A Taguchi design is a designed experiment that lets you choose a product or process that functions more consistently in the operating environment. Taguchi designs recognize that not all factors that cause variability can be controlled. These uncontrollable factors are called noise factors. Taguchi designs try to identify controllable factors (control factors) that minimize the effect of the noise factors. During experimentation, you manipulate noise factors to force variability to occur and then determine optimal control factor settings that make the process or product robust, or resistant to variation from the noise factors. A process designed with this goal will produce more consistent output. A product designed with this goal will deliver more consistent performance regardless of the environment in which it is used.
A well-known example of Taguchi designs is from the Ina Tile Company of Japan in the 1950s. The company was manufacturing too many tiles outside specified dimensions. A quality team discovered that the temperature in the kiln used to bake the tiles varied, causing nonuniform tile dimension. They could not eliminate the temperature variation because building a new kiln was too costly. Thus, temperature was a noise factor. Using Taguchi designed experiments, the team found that by increasing the clay's lime content, a control factor, the tiles became more resistant, or robust, to the temperature variation in the kiln, letting them manufacture more uniform tiles.