III. A. 7. Design of Experiments

Design of Experiments Process — A Decision Making Focus

Design of Experiments (DOE) is often only taught as a series of tools. Instead, we have used it as a tool in the Decision-Making Process which enhances implementation.

Each section below contains a 2-minute video to expand on the topic provided.

Where do we get started?

The setup and project definition are important for future success. Often, people want to jump to a solution. Then, they can start implementation and testing. Briefly pulling back to define the goal and appropriate outputs will help tremendously.

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by Fred Schenkelberg Leave a Comment

Taguchi Design of Experiments Approach

Dr. Taguchi was an engineer, not a statistician. He considered the ability of design of experiments (DOE) to identify and reduce sources of variability, yet needed a system that did not require a statistician to implement.

Dr. Taguchi proposed a few considerations for those applying the Taguchi design of experiments approach. [Read more…]

by Fred Schenkelberg 2 Comments

Taguchi’s 3 Fundamental Concepts

Taguchi may be best known for the variation of design of experiments that bear his name. Yet the impact of his work is felt across the product life cycle and all of the quality field.

Building on Deming’s observations that 85% of poor quality is due to faulty processes and only 15% due to the worker, Taguchi focused on creating robust processes.

A robust system is one that tolerates the daily and seasonal variations of the environment, machine wear, and equipment part-to-part variation, etc. A robust system operating in the range of real world conditions. [Read more…]

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 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…]

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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…]

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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…]