Case Studies and White Papers
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) .
PCR Process Optimized via Split-Plot DOE
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.
An innovative blend of hardware, software and the right training in statistical know-how supercharges research automation.
How Experimental Design Optimizes Assay Optimization
Optimizing biological assay conditions is a demanding process that scientists face daily. The requirement is to develop high-quality, robust assays that work across a range of biological conditions. The demand is to do this within a short time frame. To overcome these obstacles, automated assay optimization (AAO) systems often are used to accommodate large numbers of samples. Applying DOE to AAO is essential to make the best use of this high-tech equipment.
Keys to Successful Designed Experiments
We can improve experimentation results by studying organizations that have experienced both frustrations due to poor experimentation methodology and satisfaction from successful applications. This paper identifies eight factors essential to successful experimentation. A solid understanding of these key factors is the foundation to a successful design of experiments program.
This article explains why standard factorial designs (one array) offer a cost-effective alternative to parameter designs (two array) made popular by Taguchi. It then discusses advanced tools for robust design that involve application of response surface methods (RSM) and measurement of propagation of error (POE).
Cost-Effective and Information-Efficient Robust Design for Optimizing Processes and Accomplishing Six Sigma Objectives
Standard factorial designs (one array) offer a cost-effective and information-efficient robust design alternative to parameter designs (two-array) made popular by Taguchi. This paper compares these two methods (one-array versus two-array) in depth via an industrial case study. It then discusses advanced tools for robust design that involve application of response surface methods (RSM) and measurement of propagation of error (POE).
Quality managers who understand how to apply statistical tools for design of experiments (DOE) are better able to support use of DOE in their organizations. Ultimately, this can lead to breakthrough improvements in product quality and process efficiency.
Revealing Interactions From Fractional DOEs
Fractional two-level factorials are a powerful tool for making significant improvements to product quality and process efficiency. Unfortunately, this approach to design of experiments (DOE) may alias the main effects with their interactions. Then it is no longer clear which factors truly influence the process. In part 1, this paper illustrates the use of graphical technique for the viewing alternative aliased interactions. The graphical procedure enhances, but does not remove, the guesswork required when a highly-fractional design produces significant effects. The only sure way to pin down the actual effects will be to perform follow up experiments, which will be discussed in Part 2. A technique called "foldover" is tailor-made for de-aliasing effects. This sequential approach to DOE offers a great deal of flexibility to the quality engineer.