This paper details the fabrication of nanoparticles as an example for showing a statistically-rigorous approach to design and analysis of pharmaceutical experiments.
Researchers successfully used DOE to evaluate potential methods for removing three common endocrine disrupters. The researchers treated solutions containing the EDCs nonlyphenol (NP), bisphenol A (BPA) and triclosan (TCS) with an enzyme preparation from the white rot fungus Coriolopsis polyzona.
This presentation details and demonstrates how to plot effects from general factorials, for example a 3x4x4, on a half-normal plot. This makes selection easy and more precise by it being a graphical method. Previously the half-normal plot of effects, developed by Cuthbert Daniel, was restricted to two-level factorials.
This article starts with the basics on RSM before introducing two enhancements that focus on robust operating conditions: Modeling the process variance as a function of the input factors and Propagation of error(POE) transmitted from input factor variation. It discusses how to find the find the flats high plateaus for maximum yield and broad valleys that minimize defects. Proceeding from International SEMATECH Manufacturing Initiative (ISMI) Symposium on Manufacturing Effectiveness.
This presentation in Italian details a DOE case study on a filter cigarette packaging machine design using Design-Expert software.
This case study details how the Intertape Polymer Group (IPG) used Design of Experiments (DOE) to solve an adhesive tape production problem.
This is the third article of a series on design of experiments (DOE). The first publication provided tools for process breakthroughs via two-level factorial designs. The second article illustrated how to re-formulate rubbers or plastics using powerful statistical methods for mixture design and analysis. Via two case studies, the author now brings the focus back to process improvement. The key is in-depth DOE aimed at producing statistically-validated predictive models. Response maps made from these models point the way to pinnacles of process performance--sweet spots at high yield of in-specification products made at lowest possible cost.
This article deals with thorny issues that confront every experimenter, i.e., how to handle individual results that do not appear to fit with the rest of the data - damaging outliers and/or a need for transformation. The trick is to maintain a reasonable balance between two types of errors: (1) deleting data that very only due to common causes, thus introducing bias to the conclusions. (2) not detecting true outliers that occur due to special causes. Such outliers can obscure real effects or lead to false conclusions. Furthermore, an opportunity may be lost to learn about preventable causes for failure or reproducible conditions leading to break-through improvements (making discoveries more or less by accident).
This article describes a methodological study focused on evaluating the application of MDOE to future operational codes in a rapid and low-cost way to assess the effects of cavity geometry uncertainty.