Note: When you insert this tool into the Roadmap, you can use this form to record the data analysis from your experiment. Use the DOE Planning Worksheet form to help you design the experiment.
Provides a cost-effective methodology for conducting controlled experiments (DOEs) where all of the factors (process inputs) are held at one of two levels (settings) during each run of the experiment (plus optional center points).
The two types of 2K factorial DOEs are the 2K full-factorial and the 2K fractional-factorial:
- 2K full-factorial DOE - The experiment uses all possible combinations of factor settings with 8 runs for 3 factors, 16 runs for 4 factors, 32 runs for 5 factors, and so on. The goals of this type of experiment are usually focused on developing a full predictive model (Y = f(X)) describing how the process inputs jointly affect the process output and determining the optimal settings of the inputs.
- 2K fractional-factorial DOE - The experiment uses a fraction (one-half, one-fourth, and so on) of all possible combinations of factor settings, with a smaller number of runs than the 2K full-factorial DOE. The goals of this type of experiment can vary, from eliminating factors to developing a full predictive model (Y = f(X)) describing how the process inputs jointly affect the process output and determining the optimal settings of the inputs.
Answers the questions:
- Which process inputs (factors) have the largest effects on the process output (which inputs are the key inputs)?
- Do any important interactions between factors exist?
- Is the current testing space near an optimal condition for the process output?
- If no, what direction do you need to move to get closer to the area where the optimal condition can be found?
- If yes, what settings of the key inputs will result in the optimal process output?
- What is the equation (Y = f(X)) relating the process output to the levels of the factors?
- If I change a factor from its low setting to its high setting, how much will the process output change?
- How much of the variation in the process output can be explained by varying the process inputs?
|When to Use
||Low resolution (III or IV) 2K fractional-factorial DOEs can be used as an early screening tool to perform a first-pass elimination of noncritical inputs, especially when you have many inputs (for example, more than five) and cost or time is a significant issue.
||You can use 2K full-factorial DOEs (especially for 3 or 4 factors) and resolution V or higher 2K factorial DOEs (for 5 or more factors) to model 2-way interactions and determine the settings for the key variables that result in the optimal process output.
||If all factors are numeric and no significant curvature is present, these designs can be used to determine the direction in which to continue experimenting (to locate an area closer to the optimal solution).
||If all factors are continuous and significant curvature is present, you can expand the 2K full-factorial DOE and resolution V or higher 2K fractional-factorial DOEs to allow the fitting of a quadratic model (3-dimensional modeling using central-composite designs) to find optimal settings.
Continuous Y, categorical X's or numeric X's tested at two discrete levels.