QPRC 2016
A Synergistic Blend of Multivariate Analysis Methods with Design of Experiments Tools
​Pat Whitcomb, Stat-Ease, Inc.
Frank Westad, CAMO Software AS.
Keywords: Multivariate analysis (MVA), principal component analysis (PCA), design of experiments (DOE), optimal design.
Purpose: Provide DOE practitioners with practical tools to enhance their DOE skills.
Abstract: This talk illustrates how multivariate analysis (MVA) methods in combination with design of experiment tools can be deployed to pinpoint optimal material properties. It demonstrates how to build an optimal design from principal components that span the underlying latent structures and use the DOE results to find an optimal compound via the following steps:
- Select a set of material descriptors for a number of chemical compounds.
- Run PCA to model material properties and assess the underlying dimensionality.
- Select particular compounds for an optimal DOE.
- Perform experiments and enter the response in the DOE.
- Optimize.
- Find the compounds closest to the optimum and verify.
Attendees will come away with a better understanding of how to combine MVA and DOE.
Target Audience: DOE practitioners
