What methods are currently used for process optimization? How does one find the ideal settings when there are competing criteria? Design of experiments (DOE) is a formalized method of data collection and analysis. Computer technology has turned what used to be a series of tedious mathematical calculations into fairly quick, yet sophisticated, analyses. From these analyses, a subject-matter expert can optimize process settings by making knowledgeable tradeoffs in the response criteria. DOE can be used both in laboratory research and on the manufacturing plant floor.

Researchers and engineers who are unfamiliar with DOE can be overwhelmed by the number of design options. Designed experiments can be run to accomplish many goals. The right design must be chosen to meet the objective of the problem. Figure 1 illustrates how the objective of the design determines the type of design chosen. The discovery phase uses screening designs to identify the primary factors that control the system. These designs are especially helpful to quickly sift through a large number of possible factors whose true contributions to the system are not understood. Typically, two-level fractional-factorial designs are used in this phase, preferably something called a Resolution IV design.¹ The breakthrough phase uses either full factorial or slightly fractional factorial designs (Resolution V or higher) to positively identify both main effects and interaction effects. At this point, center points may also be added to the design in order to determine if any quadratic effects might be present. The optimization phase is required when at least some of the factors have a curvilinear relationship with some of the responses. It uses response surface designs, such as central composite (CCD) and Box-Behnken (BB) designs.² Lastly, validation runs finalize the DOE process by confirming the results in a longer-term setting.