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.
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.
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.
An industrial equipment supplier made great improvements to their corn-ethanol measurement process using DOE.
Because interactions abound in the coatings industry, the multifactor and multicomponent test matrices provided by the design of experiments (DOE) approach is very appealing. However, carrying out DOE correctly requires that runs be randomized whenever possible to counteract the bias that may be introduced by time-related trends, such as aging of materials, increasing humidity, and the like. But what if complete randomization proves to be inconvenient or impossible? In this case, a specialized form of design called “split plot” becomes attractive because of its ability to effectively group hard-to-change (HTC) factors. A split plot accommodates both HTC factors and those factors that are easy to change (ETC).
Carrying out a DOE correctly requires that runs be randomized whenever possible to counteract the bias that may be introduced by time-related trends. If complete randomization proves to be impossible, however, a specialized form of design—called a split plot—is useful because of its ability to effectively group hard-to-change (HTC) factors. It accommodates both HTC and easy-to-change (ETC) factors in the design.
By sizing experiment designs properly, test and evaluation (T&E) engineers can assure they specify a sufficient number of runs to reveal any important effects on the system. For factorial designs laid out in an orthogonal matrix this can be done by calculating statistical power. However, when a defense system behaves in a nonlinear fashion, then response surface method experiment (RSM) designs must be employed. The test matrices for RSM generally do not exhibit orthogonality, thus the effect calculations become correlated and degrade the statistical power. This in turn leads to inflation in the number of test runs needed to detect important performance differences that may be generated by the experiment. A generally acceptable alternative to sizing designs makes use of fraction of design space (FDS) plots. This article details the FDS approach and explains why it works best to serve the purpose of RSM experiments done for T&E.