2021 Volume 6 Issue 1 Pages 20-33
Screening experiments are used to identify active factors that affect the process quality of products due to the inability to study all the factors. Thus, Plackett–Burman designs are widely used for screening experiments. However, experimental runs have to be increased with a larger number of factors, which will lead to increased cost and time. Hence 2-level supersaturated designs are appropriate as the number of factors is greater than the number of runs. Based on the sparsity-of-effects principle, 2-level supersaturated designs can select a small minority of active factors from a large number of factors in screening experiments. Therefore, this study evaluates experimental design and analysis methods for 2-level supersaturated designs based on previous studies. It also proposes a guideline that can be applied to 2-level supersaturated designs ranging from experimental designs to analysis methods in screening experiments. In addition, it summarizes the optimal combinations of experimental design and analysis methods which are useful in practice.
