Use a 2K factorial DOE to provide 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).
When you insert this analysis capture tool into the Roadmap, you can use it to record the data analysis from your experiment. Use the DOE Planning form to help you design the experiment.
When to Use | Purpose |
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Mid-project | 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. |
Mid-project | 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. |
Mid-project | 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). |
Mid-project | 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. |
Your data must be values for continuous Y and categorical X values or numeric X values tested at two discrete levels.
For more information, go to Insert an analysis capture tool.
Use a general full factorial DOE to provide a methodology for conducting controlled experiments (DOEs) where the factors (process inputs) can be held at any number of levels (settings). The goals of this type of experiment are usually focused on obtaining a model that definitively selects the vital process inputs, investigating interactions between the vital inputs, and making predictions about the process output.
When you insert this analysis capture tool into the Roadmap, you can use it to record the data analysis from your experiment. Use the DOE Planning form to help you design the experiment.
When to Use | Purpose |
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Mid-project | This type of experiment is the only one that can accommodate categorical factors (process inputs) that must be investigated at more two levels. |
Mid-project | Models main effects and all possible interactions between factors, which is beneficial for determining the settings for the key inputs resulting in the optimal process output. |
Your data must be values for continuous Y and categorical or numeric X values tested at two or more discrete levels.
For more information, go to Insert an analysis capture tool.
Use a mixture DOE to provide a cost-effective methodology for the evaluation of factors whose sum total volume or quantity cannot change. For example, if you wish to add more fruit filling to an 8-ounce fruit bar, another ingredient must be reduced. Such adjustments are common in packaged food and chemical formulations. The goals of this type of experiment are usually focused on developing a full predictive model (Y = f(X)) describing how the ingredients in the mixture jointly affect the process output and determining the optimal amounts of each ingredient.
When to Use | Purpose |
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Mid-project | If you believe the desired characteristics of the mixture are a function of only the ingredients, use a pure mixture DOE to evaluate which ingredients have the largest influence on the characteristics, build a predictive model using the key ingredients, and find the optimal quantities of the ingredients. |
Mid-project | If you believe the desired characteristics of the mixture are a function of both the ingredients and the process, use a mixed model DOE (some factors are ingredients, some are process inputs) to evaluate which ingredients and process inputs have the largest influence on the characteristics. Then, build a predictive model using the key ingredients and key process inputs and find the optimal quantities of the ingredients along with the optimal settings of the process inputs. |
Your data must be a continuous value for Y and continuous Xs (for a pure mixture design).
If you have discrete numeric data from which you can obtain every equally spaced value and you have measured at least 10 possible values, you can evaluate these data as if they are continuous.
For more information, go to Insert an analysis capture tool.
Use multiple response optimization to determine the optimal settings in an experiment with a single output, or with multiple competing outputs. It also provides a graphical tool for exploring what-if alternative solutions. A desirability function is created for each process output, with multiple outputs combined into an overall desirability using adjustable weights for each output.
When to Use | Purpose |
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Mid-project | Very useful for determining the settings of key process inputs that result in the optimal value of a single process output. |
Mid-project | Very useful for determining the settings of key process inputs that result in the best compromise solution for satisfying the goals relative to two or more process outputs. |
Mid-project | Make adjustments to the initial optimal solution, determine the impact on the outputs and the compromise, and settle on a final optimal solution. |
Your data must be a 2K DOE, response surface DOE, or a mixture DOE solved for one or more outputs.
If you have discrete numeric data from which you can obtain every equally spaced value and you have measured at least 10 possible values, you can evaluate these data as if they are continuous.
For more information, go to Insert an analysis capture tool.
Use a response surface DOE to provide a cost-effective methodology for conducting controlled experiments (DOEs) in cases where there is believed to be curvature and all the factors are continuous and can be tested at (usually) three to five levels. 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.
When to Use | Purpose |
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Mid-project | If the number of process inputs to be investigated is small (typically less than seven), you can run these designs by adding new test runs to an existing 2-level full or fractional factorial design when the 2-level factorial design shows evidence of curvature. All factors must be continuous. |
Mid-project | When all the factors are continuous and show significant curvature, these designs are used because they allow the fitting of quadratic terms to model the curvature, resulting in better interpolation between design points and an improved search for the optimal settings. |
Your data must be a continuous value for Y and continuous Xs tested at three to five discrete levels.
For more information, go to Insert an analysis capture tool.