Case Studies and White Papers
Mix ordinary white glue (Elmer's Glue) and a cross-linking agent: borax (20 MULE TEAM brand from your local grocery store). Eureka! You've made play putty. To make things more interesting, add laundry starch. (STA-FLO concentrated liquid) to the mixture. See how well you can do with this home-made material in comparison to the real thing sold commercially as a toy: Silly Putty.
This article offers a simple case study that illustrates how to put rubber or plastics formulations to the test by using powerful statistical methods for mixture design and analysis. Rubber & Plastics News.
Screening Ingredients Most Efficiently with Two-Level Design of Experiments (DOE)
A DOE on machine-made bread shows how clever application of statistical methods quickly screens alternative ingredients to see which, if any, impair the desired reaction.
This kitchen experiment on a bread-baking machine illustrates the power of multifactor testing for unveiling breakthrough interactions. The surprising results from the original two-level fractional factorial were confirmed by an innovative follow-up experiment called a "semi-foldover".
Cost-Effective and Information-Efficient Robust Design for Optimizing Processes and Accomplishing Six Sigma Objectives
Standard factorial designs (one array) offer a cost-effective and information-efficient robust design alternative to parameter designs (two-array) made popular by Taguchi. This paper compares these two methods (one-array versus two-array) in depth via an industrial case study. It then discusses advanced tools for robust design that involve application of response surface methods (RSM) and measurement of propagation of error (POE).
How to Save Runs, Yet Reveal Breakthrough Interactions, By Doing Only a Semifoldover on Medium-Resolution Screening Designs
Via case studies, this paper reviews the strategy of foldover on low-resolution (III) two-level fractional factorials and demonstrates how to reduce experimental runs by making use of semifoldover methods to augment medium-resolution (IV) designs.
Achieving Six Sigma Objectives for Variability Reduction in Formulation and Processing
Apply powerful design of experiments (DOE) tools to make your system more robust to variations in component levels and processing factors.
Design Experiments that Combine Mixture Components with Process Factors
This article shows how to do a comprehensive experiment that combines mixture components with process factors in one crossed design, thus revealing interactions that would remain hidden by not combining all the variables in one study.
Practical Aids for Teaching Experimental Designs
Design of Experiments (DOE) is an essential tool for product and process improvement. Good software now makes the set up for design and analysis of experiments very easy, but many engineers and/or non-statisticians feel intimidated by statistical outputs. For that reason, non-statisticians need training in proper designing and conducting of experiments. Ideally the DOE training is best when provided on a just-in-time basis - prior to actually doing an experiment. However, an in-class experiment is a reasonable substitute.
(This is the manuscript submitted for publication.)
How to Design and Analyze Mixture Designs that Include Process Factors and/or Categorical Variables
The latest versions of dedicated DOE software exhibit more versatility than ever before to create optimal designs that handle any combination of mixture components, processing factors (such as time or temperature) and categorical variables (such as supplier and material type). These computer programs easily manipulate almost any number of responses in powerful optimization routine that reveal "sweet spots" - the operating windows that meet all specifications at minimal cost. In this paper, we review the basic principles of mixture design. Then we apply state-of-the-art tools for optimal design to the formulation of a coating.