With the latest computer software, today's formulators can take advantage of a powerful tool: design of experiments (DOE) for mixtures. DOE methods use test arrays that produce maximum information from minimal runs. Industrial experimenters typically turn to two-level factorials as their first attempt at DOE. However, mixture design accounts for the dependence of response on proportionality of ingredients where factorial design does not. If you formulate chemicals, food or other products, consider using mixture design rather than factorials or related optimization methods. To show you how, follow along as we conduct a kitchen chemistry experiment on pound cake
In many rubber and plastics processes, powerful interactions affect final performance. These remain undiscovered via traditional one-factor-at-a-time scientific methods. Multifactor design of experiments (DOE) reveals these interactions that lead to breakthrough improvements in process efficiency and product quality. The big gains come from a very simple form of DOE called two-level factorial design. This approach to experimentation has proven to be especially helpful for control of part shrinkage as demonstrated in a case study. However, it can be applied to any measurable response in rubber and plastics production. This primer provides the essential details on two-level factorial DOE from an engineering perspective with an emphasis on the practical aspects.
In many rubber and plastics processes, powerful interactions affect final performance. You will not discover interactions when you change only one factor at a time. Proper design of experiments (DOE) will reveal interactions that can help you achieve breakthrough improvements in process efficiency and product quality. The big gains come from a simple form of DOE called two-level factorial design. This approach to experimentation has proven helpful in controlling part shrinkage, but it can be applied to any measurable response. In this article you will be given the primary details from an engineering perspective.
Engineers at an aluminum-casting company were struggling to understand why a particular part came off the line filled with inclusions. Having conducted many one-factor-at-a-time tests to no avail, they turned to statistical software and a process called design of experiments. Optimizing based on this process let the engineers reduce the defect rate to zero.
A somewhat different version of this article appeared in Modern Paint and Coatings.
What would you do it confronted with an "opportunity" to make a major change, involving many factors, but you need to do it quickly? The traditional approach to experimentation requires you to change only one factor at a time (OFAT). However, the OFAT approach doesn’t provide data on interactions of factors, a likely occurrence with chemical processes. An alternative approach called “two-level factorial design” can uncover critical interactions. This statistically based method involves simultaneous adjustment of experimental factors at only two levels, offering a parallel testing scheme that’s much more efficient than the serial approach of OFAT.
G.C. Derringer provides an easy-to-read explanation of the commonly used optimization function called desirability. When used as the final step in DOE, this function allows simultaneous optimization of multiple responses, resulting in the discovery of a group of optimal factor settings.
Design of experiments identifies which factors matter and which ones don't when microwaving popcorn, as well as helping find optimal settings.
This presentation details and demonstrates a procedure that, despite missing data, allows the use of user-friendly, normal-probability plots for two-level-factorial effect selection.