Rapid Development of New Polymer Blends:  The Optimal Selection of Materials and Blend Ratios

Abstract

A data-based approach to the development of industrial polymer blends with specified final properties is presented. There are basically three major degrees of freedom to control the final product properties: the selection of raw materials, the selection of the ratios in which to blend them, and the selection of process conditions used to manufacture them. In this paper, we present a new optimization approach that simultaneously addresses all of these degrees of freedom, but the primary focus will be on the selection of the materials and their ratios. The approach involves building partial least-squares (PLS) models that combine databases on previously made blends and databases on the properties of the component materials used in these blends. The resulting models are then used in an optimization framework to select raw materials from much larger databases (including materials never previously used) and to select the ratios in which to blend them in order to yield a blend product with specified end properties at a minimum cost. The methodology is applied to two industrial polymer blending problems that involve the replacement of raw materials while keeping the same product properties and minimizing total raw material cost.

Sequential design of mixture experiments for the development of new products

“…Muteki et al [1][2][3] presented a combined mixture-property modeling approach based on ideal mixing rules that allows for the incorporation of information on the raw material properties (X) as well. The model is expressed as…”

“…In cases where ideal mixing rules may not be adequate, but better mixing rules are unknown, Muteki and MacGregor [6] have proposed the use of multi-block, nonlinear PLS methods for the L-shaped data structure of Figure 1. There are many advantages to the above mixture-property modeling approaches that use both the ratios R and the material properties X T , compared to the traditional mixture PLS model that use only R [3], the most important for this paper being that it allows for the simultaneous selection of raw materials and their ratios in an optimization framework [1,2].…”

“…It is assumed that one has existing experiments, some of which may not be too far from those required for the desired new product. To accomplish this we present a strategy based on the general rapid product development approach of Muteki et al [1,2] that simultaneously takes into account the selection of raw materials, the ratios in which to use them and the conditions under which to process them. In order to obtain successful results with that general approach [1,2], it is required that the data on the blends are sufficiently rich.…”

“…To accomplish this we present a strategy based on the general rapid product development approach of Muteki et al [1,2] that simultaneously takes into account the selection of raw materials, the ratios in which to use them and the conditions under which to process them. In order to obtain successful results with that general approach [1,2], it is required that the data on the blends are sufficiently rich. Muteki et al [3] present a mixture-property DOE approach that effectively combines statistical molecular design (SMD) and D-optimal mixture design methods to ensure a sufficiently rich database for this product development purpose.…”

“…In Section 2, the data structure generally available in mixture design is first shown, and the mixture-property partial least square (PLS) model [1][2][3] is briefly described. In Section 3, the new sequential DOE approach with the objective of achieving a set of specified final product properties is presented.…”