Application of Design of Experiment Based Approach in Coatings Research

Latex paints are complex formulations wherein a large number of ingredients are mixed and stabilized to optimize performance against multiple responses related to protection and aesthetics of substrates. Interactions among formulation parameters such as the binder type, additive package, percent volume solids, and pigment volume concentration significantly impact the final paint performance. In order to understand these complex interactions, a design-of-experiments approach can be leveraged to probe multiple factors and their higher order interactions while minimizing the requisite number of samples needed for study. Here we use such an approach to build predictive models for film performance properties across a range of formulation factors and also estimate the trends and associations of these factors. Two case studies are discussed showcasing optimal design-of-experiments strategies, applying Taylor-Series Expansion estimation models encompassing main effects and higher order interaction terms involving multiple factors and levels. The first study focuses on applied hiding and surface smoothness of a final applied film where rheology modifier types, levels, and volume solids of the paint formulations vary. The second study focuses on the impact of blending latexes of two different chemistries on multiple paint properties utilizing high throughput methodologies. In both studies, predictive models estimate the trends in the responses associated with the variables. Optimized levels identified for significant factors are validated to achieve a targeted balance of properties.