New hybrid multivariate analysis approach to optimize multiple response surfaces considering correlations in both inputs and outputs

• Autor(a) principal: Hejazi, Taha Hossein
• Data de Publicação: 2014
• Outros Autores: Seyyed-Esfahani, Mirmehdi, Ramezani, Majid
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Acta scientiarum. Technology (Online)
• Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/17532
• topic: correlated multi-response optimization; correlated covariates; simultaneous equation systems; principal component analysis (PCA); global criterion (GC) method

Quality control in industrial and service systems requires the correct setting of input factors by which the outputs result at minimum cost with desirable characteristics. There are often more than one input and output in such systems. Response surface methodology in its multiple variable forms is one of the most applied methods to estimate and improve the quality characteristics of products with respect to control factors. When there is some degree of correlation among the variables, the existing method might lead into misleading improvement results. Current paper presents a new approach which takes the benefits of principal component analysis and multivariate regression to cope with the mentioned difficulties. Global criterion method of multiobjective optimization has been also used to reach a compromise solution which improves all response variables simultaneously. At the end, the proposed approach is described analytically by a numerical example.