Articles tagged Design of Experiments (DOE)

The arrangement in which an experimental program is to be conducted and selection of the levels of one or more factors or factor combinations to be included in the experiment. It is also known as statistical design of experiment, as it entails a versatile statistical approach to screen for important factors, optimize factor settings, or detect important factor relationships or interactions.

by Fred Schenkelberg Leave a Comment

When is DOE Useful?

On page 6 of Statistics for Experimenters, Box, and Hunter suggest a scientist could conduct an investigation without statistics.

Whereas, a statistician could not do so without scientific knowledge.

The text’s discussion quickly expands on the benefits to a scientist when they do employ statistical thinking and tools.

Design of Experiments, DOE, is a set of statistical tools that allow an investigator to efficiently examine multiple factors and their associated influence on results.

DOE allows us to be better investigators. [Read more…]

by Fred Schenkelberg Leave a Comment

Design of Experiments and Reliability

During the design phase, we make decisions that create the eventual reliability performance of a product.

It is the decisions we make that matter.

Also during the design phase, we explore numerous questions. [Read more…]

by Fred Schenkelberg 5 Comments

Analyzing a Taguchi L4 Array Experiment

We’ve collected data and it’s time for the analysis.

As you may recall, in the last article on Planning a Taguchi L4 Array Experiment, we drafted a set of four prototypes. The specific arrangement of factors and levels will now allow us to analyze each factor separately.

The intent is to find the optimal level or setting for each factor, plus which is the most important factor. [Read more…]

by Fred Schenkelberg Leave a Comment

Planning a Taguchi L4 Array Experiment

One of the simplest ways to learn design of experiments, DOE, is to just give it a try.

The Taguchi DOE approach uses orthogonal arrays. This subset of the possible approaches to DOE simplifies the process to create and analyze experiments.

Let’s plan a simple experiment using the Taguchi DOE approach. [Read more…]

by Fred Schenkelberg Leave a Comment

Confounded DOE

A simple assumption in many experiments is to assume the variable act independently on the response.

This means when I change the temperature a little in a polymer dryer silo that time to achieve a certain dryness goes down. And, changing the humidity or airflow rate or pressure either do not change or as they change has no impact on the relationship between temperature and drying time. [Read more…]

by Richard Coronado Leave a Comment

Design of Experiments

Design of Experiments (DoE) and the Analysis of Variance (ANOVA) techniques are economical and powerful methods for determining the statistically significant effects and interactions in multivariable situations. DoE may be utilized for optimizing product designs, as well as for addressing quality and reliability deficiencies. Within the DoE framework, the practitioner may explore the effects of a single variable or analyze multiple variables. [Read more…]