Design Considerations

Experiment Design Considerations

Situations, where experimental design can be effectively used include:

• Choosing between alternatives

• Selecting the key factors affecting a response

• Response surface modeling to hit a target

• Reduce variability

• Maximize or minimize a response

• Make a process robust (despite uncontrollable “noise” factors).

• Seek multiple goals Experiment

Design Considerations

DOE Steps, getting good results from a DOE involves a number of steps

• Set objectives

• Select process variables

• Select an experimental design

• Execute the design

• Check that the data are consistent with the

• Analyze and interpret the results.

• Use/present the results (may lead to further runs or DOEs) Experiment.

Practical considerations in planning and running experiments are:

• Check the performance of gauges/measurement devices first.

• Keep the experiment as simple as possible

• Check that all planned runs are feasible

• Watch out for process drifts and shifts during the run

• Avoid unplanned changes (e.g. Switching operators at half time)

• Allow some time (and backup material) for unexpected events

• Obtain buy-in from all parties involved

• Maintain effective ownership of each step in the experimental plan

• Preserve all the raw data - do not keep only summary averages

• Record everything that happens

• Reset equipment to its original state after the experiment

Experiment Design Considerations

Select and Scale the Process Variables, Process variables include both inputs and outputs, i.e. factors and responses. The selection of these variables is best done as a team effort. The team should: Include all important factors (based on engineering and operator judgments).

• Be bold, but not foolish, in choosing the low and high factor levels.

• Avoid factor settings for impractical or impossible combinations.

• Include all relevant responses.

• Avoid using responses that combine two or more process measurements When choosing the range of settings for input factors, it is wise to avoid extreme values.

• In some cases, extreme values will give runs that are not feasible; in other cases, extreme ranges might move the response surface into some erratic region.

Two-Level Designs:

The most popular experimental designs are called two-level designs. Two-level designs are simple and economical and give most of the information required to go to a multi-level response surface experiment if one is needed. However, two-level designs are something of a misnomer. It is often desirable to include some center points (for quantitative factors) during the experiment (center points are located in the middle of the design “box.”).