This article provides an introduction to design of experiments (DOE) for improvement of coatings processes and formulations. It includes a case study on a spin-coater.
This paper demonstrates response surface methods (RSM) that build on subject-matter knowledge to objectively fine-tune processes to a sweet spot where all specifications are met. The RSM tools for design and analysis of experiments is illustrated by a case study on process improvement for a pressure-sensitive adhesive (PSA) .
This paper provides an overview of DOE, including response surface methods (RSM), applied to medical-device process validation. It provides two practical examples of manufacturing application. The benefits of this multi-factor approach to medical-device validation are spelled out in a convincing manner.
A local automotive seat supplier experienced a major quality problem in the fabrication of the metal seat frame used for the front bucket seats of automobiles. DOE was used to help solve the quality problem.
Design of Experiments (DOE) is an intrinsically hands-on topic; teaching students to apply the techniques in a classroom setting can be difficult. Especially difficult are students with differing backgrounds, for example, adult students that work full time in various industries but come together in the classroom. Finding examples that everyone can relate to is nearly impossible, so why not try a video game?
The introduction of Aventis CropScience's herbicide should have been a routine pilot-to-production transfer, but an there was a problem: the new herbicide clogged spray application equipment. Scientists initiated a series of designed experiments to analyze how diverse factors in the system were interacting.
Aided by DOE, scientists at Diosynth Biotechnologies established critical parameters and their acceptable ranges for a fermentation process. The results enabled more flexibility for manufacturing an active pharmaceutical ingredient (API).
The first article, "Trimming the FAT out of Experimental Methods," presented arguments against one-factor-at-a-time (OFAT) techniques in favor of multifactor DOE. This follow-up article offers a case study that illustrates how a two-level factorial DOE can reveal a breakthrough interaction. A similar article appeared in OE (Optical Engineering) magazine.
This introductory article provides compelling reasons to abandon traditional scientific methods that deploy only one factor at a time (OFAT) in favor of multifactor testing techniques known as design of experiments (DOE). Only via DOE can experimenters detect interactions, which often prove to be the key to success.
This paper illustrates the use of design of experiments (DOE) and split-plot design to quickly and effectively determine the factor settings that maximize amplification in a polymerase chain reaction (PCR) experiment.