This article details a delightful experiment that can be done at home or in class to illustrate the advantage of multifactor testing over the traditional one-factor-at-a-time (OFAT) scientific method. It uncovers multiple interactions that surprisingly cancel out OFAT main effects.
Energized by new tools in version 13 of Design-Expert (DX13) for modeling counts, Engineering Consultant Mark Anderson tests a cellphone app against built-in timing on his microwave for minimizing unpopped kernels (UPK). DX13 paves the way to nearly perfect popcorn via its Poisson-regression count-modeling capability.
Ever-increasing demand for monoclonal antibodies (mAbs) makes it imperative that their production be continually improved for cost, quality and yield. Design of experiments (DOE), by its multifactor testing methodology and statistical rigor, provides a sure path to mAb process optimization. This was demonstrated recently in a series of tests at a biotechnology company. By using the tools of DOE versus the traditional scientific method of one-factor-at-a-time (OFAT) experimentation, its mission was achieved in a matter of weeks rather than months with a far more comprehensive mapping of process conditions.
Researchers at Adverum Biotechnologies have demonstrated that a novel multifactor design of experiments (DOE) methodology can optimize production of a recombinant adeno-associated virus (rAAV) vector. The methodology, an advanced form of DOE that incorporates response surface methods (RSMs), focused on the relative amounts of transfection agent polyethylenimine (PEI) and DNA.
This methodology is unique because of the way it keeps the ratio of PEI nitrogen to DNA phosphate—the N/P ratio—within a specified range. The N/P ratio is well known to influence the rAAV yields obtained via transient transfection.
Industrial product development can often be a frustrating process, especially in the case of formulations. Many commercial formulated products can have 20 or more components. With so many possibilities for multi-component blending effects to impact performance, it is difficult to optimize a formulation without design of experiments (DOE).
This case study explores how chemists at Quaker Houghton (Conshohocken, PA) explored reformulating an existing product to improve performance and reduce cost. Their use of Design-Expert led to discoveries they didn't expect.
Case studies, complete with data, that cover a wide range of applications of DOE in engineering and science. The collection is ideal for teachers and students of DOE. It is also useful for those who want to learn more about the power of DOE methods or who are looking for research ideas.
By way of example, this article lays out a strategy for design of experiments (DOE) that provides maximum efficiency and effectiveness for development of a robust system. It broadens the scope of a prior article (Anderson and Whitcomb 2014) that spelled out how to right-size multifactor tests via statistical power-calculations—a prerequisite for DOE success.
Without mixture design of experiments, chemists would have taken twice as long to develop the extremely biostable HOCUT 8000 coolant and been lucky to achieve its same levels of performance in extending sump life and eliminating the need for costly sump-side additives. Here's an inside look at how they did it.
Engineers at a major medical device manufacturer used RSM to successfully model a key process for their flagship product. The RSM model then became the foundation for development of robust specifications to ensure quality at six-sigma levels.