ABSTRACT
In many industrial trials, the second-order models may not be enough to fit the non linearity of the underlying model, and the third-order models may be considered. In this article, the orthogonal-array composite design (OACD), combined with two-level OA and four-level OA and denoted by OACD4, is proposed to estimate the second-order and third-order models. It is shown that OACD4 has good properties and has higher efficiency than other types of designs for the third-order models, and OACD4 can perform multiple analysis for cross-validation. The usefulness of OACD4 is also shown by a case study for polymer synthesis experiment.
MATHEMATICS SUBJECT CLASSIFICATION:
Funding
This work was supported by CEMEE State Key Laboratory fund project (CEMEE2015Z0301A1), National Natural Science Foundation of China (11471229), and Fundamental Research Funds for the Central Universities (2013SCU04A43).