Taguchi designs - Minitab. 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 1. 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. Taguchi designs use orthogonal arrays, which estimate the effects of factors on the response mean and variation. An orthogonal array means the design is balanced so that factor levels are weighted equally. Because of this, each factor can be assessed independently of all the other factors, so the effect of one factor does not affect the estimation of a different factor. This can reduce the time and cost associated with the experiment when fractionated designs are used. CHAPTER 2 INTRODUCTION TO TAGUCHI METHOD 2.1 Background. The method is popularly known as the factorial design of experiments. A full factorial design will identify all possible combinations for a given set of factors. 32.3 Taguchi’s Robust Design Method. Taguchi methods are used to specify dimension and feature. Orthogonal array designs concentrate primarily on main effects. Some of the arrays offered in Minitab's catalog let a few selected interactions to be studied. You can also add a signal factor to the Taguchi design in order to create a dynamic response experiment. A dynamic response experiment is used to improve the functional relationship between a signal and an output response. Session window output for a Taguchi design. Minitab calculates response tables, linear model results, and generates main effects and interaction plots for. S/N ratios, which provide a measure of robustness) vs. To get a complete understanding of factor effects, you should usually assess signal- to- noise ratios, means (static design), slopes (Taguchi dynamic design), and standard deviations. Ensure that you choose a signal- to- noise ratio that is appropriate for the type of data you have and your goal for optimizing the response. Note. If you suspect curvature in your model, select a design - such as 3- level designs - that lets you detect curvature in the response surface.
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