Proposal to Optimizing Design Using [a, b] Analysis Considering Interaction for Design Matrices Experiment

HadamardHadamard orthogonal tables correspond to interactionsInteraction in linear graphs. But it only represents part of the interaction. Mixed orthogonal arraysOrthogonal array are highly confounding to the main effectsMain effect of interactionsInteraction. These experimental design methods capture the main effects, but cannot grasp the interaction. About 62% of the optimal conditions for the mixed systemSystem orthogonal arrayOrthogonal array are below the best experimental no. value. This chapter proposes a method for selecting optimal conditionsCondition that considers both interactionsInteraction and main effectsMain effect. We compared the no. best value conditionCondition (a) and the best levelLevel conditionCondition (b) of the factor effectEffect in the orthogonal arrayOrthogonal array experiment and designed it assuming that the difference levelLevel factor was strongly related to the interactionInteraction and the common level factor to the main effectMain effect. The method is described in detail as [a, b] analysis[a,b]Analysis.