In Taguchi designs, noise factors are factors that cause variability in the performance of a system or product, but cannot be controlled during production or product use. You can, however, control or simulate noise factors during experimentation. You should choose noise factor levels that represent the range of conditions under which the response should remain robust.
During experimentation, you manipulate noise factors to force variability to occur, then from the results, identify optimal control factor settings that make the process or product resistant, or robust to variation from the noise factors. Control factors are those design and process parameters that can be controlled.
For example, a printer manufacturer wants to optimize printer performance. One noise factor is different paper types. During experimentation, the manufacturer tests several paper types to determine control factors that reduce the effect of paper type on printer performance.
Compounding noise factors is a strategy in which you group the noise factor levels into combinations that you anticipate will produce extreme response values. Because estimating the effects of individual noise factors is not the primary goal, compounding is a useful way to reduce the amount of testing. For example, if you have three noise factors, each with two levels, you could have eight different combinations of settings to test. Instead, you could group noise factors into two overall settings – one setting in which the noise factors levels increase the response values and the other setting in which the noise factors levels decrease the response values.
A signal factor is a factor, with a range of settings, that is controlled by the user during use. A signal factor is present in a dynamic Taguchi design, but is not present in a static Taguchi design. In a dynamic response design, the quality characteristic operates along a range of values and the goal is to improve the relationship between a signal factor and an output response. In a static response design, the quality characteristic of interest has a fixed level.
For example, the amount of deceleration is a measure of brake performance. The signal factor is the degree of depression on the brake pedal. As the driver pushes down on the brake pedal, deceleration increases. The degree of pedal depression has a significant effect on deceleration. Because no optimal setting for pedal depression exists, it is not logical to test it as a control factor. Instead, engineers want to design a brake system that produces the most efficient and least variable amount of deceleration through the range of brake pedal depression.