A unique solution for principal component analysis-based multi-response optimization problems

作者：Fard Nasser^*; Xu Huyang; Fang Yuanchen

来源：International Journal of Advanced Manufacturing Technology, 2016, 82(1-4): 697-709.

DOI：10.1007/s00170-015-7358-x

• Multi-response optimization
• PCA
• Indices
• Eigenvector
• Factor levels

A procedure to find a unique solution for multi-response optimization problems based on indexing is presented. The procedure utilizes principal component analysis to map the original data to a new vector of component scores, transforming the original response variables into uncorrelated principal components. This process involves loadings that are the elements of the eigenvectors corresponding to the eigenvalues of response variables in the correlation matrix. It is shown that for a given eigenvalue lambda, its corresponding eigenvectors are not unique, which could lead to different "optimal" parametric (factor-level) settings and will further mislead the process or product improvement strategy. The proposed indexing method will determine a unique optimal solution in the presence of (2(p))(p!) combinations of eigenvectors.

• 出版日期2016-1
• 单位东北大学