This research presents a new method to generate near-orthogonal balanced mixed-level fractional designs. The proposed method showed that it is possible to create near-orthogonal balanced fractions of economic size. The method is based in the analysis of the behavior of the genetic algorithm used to generate the efficient arrays (EAs) developed by Guo; a pattern was detected, and this led to the generation of an algorithm capable of constructing fractions in a simple way. These fractions were called near-orthogonal balanced arrays (NOBAs). To analyze the properties of the NOBAs and the capabilities of the proposed method, a series of performance indicators were defined. The NOBAs were compared with the EAs developed by Guo; results are provided.

Blocking is a basic experimentation principle that separates the variability caused by noise factors. Unless the experiment is replicated, blocking produces loss of information, particularly two‐factor interactions (2FIs) are commonly lost to blocks. Additionally, the ANOVA table does not show to what extent each blocked noise factor affects the response variable. Individual contributing percentages for noise factors can be useful to make process improvements and to understand which noise factors are most influential. This research proposes a sequential experimentation method to separate 2FIs from blocks and assign contributing percentages to each blocked noise factor. The method is evaluated and compared to foldover, semifold, and D‐optimal augmentation.

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